# Torch
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader, Subset, random_split
from tqdm import tqdm

# Augmentation
import albumentations as A
from albumentations.pytorch import ToTensorV2
import scipy.io as sio

# Visualize Result
from sklearn.metrics import (confusion_matrix, accuracy_score,
                            precision_score, recall_score,
                            f1_score, roc_auc_score,
                            roc_curve, auc, precision_recall_curve,
                            average_precision_score)
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.preprocessing import label_binarize

# Basic
import numpy as np
import cv2
import os
import time
from typing import List, Tuple, Union
import random
import itertools

device_name = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(device_name)
print(f"Using device: {device_name}")

Using device: cuda

path_dataset = "./data/SVHN_mat"

class SVHNDataset(Dataset):
    def __init__(self, mat_file, transform=None):
        data = sio.loadmat(mat_file)
        self.images = np.transpose(data['X'], (3, 0, 1, 2))
        self.labels = data['y'].flatten()
        self.labels[self.labels == 10] = 0
        self.transform = transform

    def __len__(self):
        return len(self.labels)

    def __getitem__(self, idx):
        image = self.images[idx]
        label = self.labels[idx]

        if self.transform:
            image = self.transform(image=image)['image']
            
        return image, label

    def get_meanstd(self):
        images_ = self.images.astype(np.float32) / 255.0
        mean = np.mean(images_, axis=(0,1,2))
        std = np.std(images_, axis=(0,1,2), ddof=0)
        
        return mean.tolist(), std.tolist()

def peek(dataset):
    def unnormalize(img, mean, std):
        """Revert the normalization for visualization."""
        img = img * std + mean
        return np.clip(img, 0, 1)

    # Plotting multiple images in a grid
    grid_rows, grid_cols = 1, 6
    
    fig, axes = plt.subplots(grid_rows, grid_cols, figsize=(6, 6))
    
    peek_index = random.randint(0, dataset.__len__()-1)
    
    for i in range(grid_cols):
        img_tensor, label = dataset.__getitem__(peek_index)
        img = img_tensor.permute(1, 2, 0).numpy()  # Convert to (H, W, C)
        img = unnormalize(img, norm_mean, norm_std)
    
        ax = axes[i]  # Get subplot axis
        ax.imshow(img)
        ax.set_title(f"Label: {label}")
    
    plt.tight_layout()
    plt.show()
    print(f"Peaking data from training set of index {peak_index}.\nImage Tnesor Size:{train_dataset.__getitem__(peak_index)[0].shape}")

class SmallVGG(nn.Module):
    def __init__(self, frame_size=32):
        super(SmallVGG, self).__init__()
        self.frame_size = frame_size
        self. conv_layers = nn.Sequential(
            nn.Conv2d(3, 8, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(8, 16, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2), # 16x16

            nn.Conv2d(16, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2), # 8x8

            nn.Conv2d(32, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2), # 4x4
        )
    
        self.fc_layers = nn.Sequential(
            nn.Linear(frame_size * 4 * 4, 256),
            nn.ReLU(),
            nn.Linear(256, 10)
        )

    def forward(self, x):
        x = self.conv_layers(x)
        x = x.view(x.size(0), -1)
        x = self.fc_layers(x)
        return x

def train_and_evaluate(model,
                      train_loader,
                      valid_loader,
                      criterion,
                      optimizer,
                      num_epochs=100):
    # Record Losses to plot
    train_losses = []
    valid_losses = []

    for epoch in range(num_epochs):
        # Train
        model.train()
        running_loss = 0.0
        for images, labels in tqdm(train_loader):
            images, labels = images.to(device), labels.to(device)

            optimizer.zero_grad()
            outputs = model(images)

            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            running_loss += loss.item() * len(images)
        train_losses.append(running_loss / len(train_loader))

        # Evaluate
        model.eval()
        valid_loss = 0.0
        with torch.no_grad():
            for images, labels in valid_loader:
                images, labels = images.to(device), labels.to(device)
                outputs = model(images)
                loss = criterion(outputs, labels)
                valid_loss += loss.item() * len(images)

        valid_losses.append(valid_loss / len(valid_loader))
        print(f"Epoch[{epoch+1}/{num_epochs}], Train Loss:{train_losses[-1]:.4f}, Validation Loss:{valid_losses[-1]:.4f}")

    return train_losses, valid_losses

def get_predictions(model_path, extra_loader):
    if not isinstance(model_path, str):
        model_state = model_path
    else:
        model_state = torch.load(model_path)
    model = SmallVGG()
    model.load_state_dict(model_state)
    
    model.to(device)
    model.eval() 
    
    pred_scores = []  # Prob. of predictions
    true_labels = []  # Ground Truth
    pred_labels = []  # Label of prediction, i.e., argmax(softmax(pred_scores))
    
    with torch.no_grad():
        for images, labels in tqdm(extra_loader):
            images, labels = images.to(device), labels.to(device)
        
            outputs = model(images)
    
            pred_scores_batch = nn.functional.softmax(outputs, dim=-1)
    
            pred_scores.extend(pred_scores_batch.cpu().tolist())
            pred_labels.extend(outputs.argmax(dim=1).tolist())
            true_labels.extend(labels.cpu().tolist())
            
    return pred_scores, true_labels, pred_labels

def get_metrics(true_labels, pred_labels):
    accuracy = accuracy_score(true_labels, pred_labels)
    precision = precision_score(true_labels, pred_labels, zero_division=1, average=None, labels=range(0,10))
    recall = recall_score(true_labels, pred_labels, zero_division=1, average=None, labels=range(0,10))
    f1 = f1_score(true_labels, pred_labels, zero_division=0, average=None, labels=range(0,10))

    return accuracy, precision, recall, f1

def display_cm(true_labels, pred_labels):
    cm = confusion_matrix(true_labels, pred_labels)
    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=range(0,10))
    disp.plot(cmap=plt.cm.Blues)
    plt.show()

display_cm(true_labels_cpu, pred_labels_cpu)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[117], line 1
----> 1 display_cm(true_labels_cpu, pred_labels_cpu)

NameError: name 'true_labels_cpu' is not defined

# Compute ROC AUC for each class
def get_roc_auc(true_labels_bin, pred_labels_bin):
    roc_auc = dict()
    for i in range(0, 10):
        roc_auc[i] = roc_auc_score(true_labels_bin[:,i], np.array(pred_scores)[:, i])
    return roc_auc

{
    "HYPER_PARAM_1": combo[0],
    "HYPER_PARAM_2": combo[1],
    "train_losses": train_losses,
    "test_losses": test_losses,
    "model_state_dict": exp_model.state_dict()
}

def plot_el(loaded_experiments, hyper_param_names, n_rows=4, n_cols=4):
    fig_size = (n_cols * 5, n_rows * 5)
    n1, n2 = hyper_param_names
    
    fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=fig_size)
    # plt.tight_layout()
    
    for i, ax in enumerate(axes.flat):
        train_losses, valid_losses = loaded_experiments[i]["train_losses"], loaded_experiments[i]["test_losses"]
        
        ax.plot(train_losses, label=f"TRL, min={np.min(train_losses):.3f}")
        ax.plot(valid_losses, label=f"VAL, min={np.min(valid_losses):.3f} at step={np.argmin(valid_losses)}")
        ax.set_xlabel("Epochs")
        ax.set_ylabel("Loss")
        ax.set_title(f"{n1}={loaded_experiments[i][n1]}, {n2}={loaded_experiments[i][n2]}")
        ax.legend(loc="upper right")
    
    plt.show()

def get_experiment_results(loaded_experiments, test_hyperparam_names, extra_loader):
    experiment_results = []
    n1, n2 = test_hyperparam_names
    for i, exp in enumerate(loaded_experiments):
        pred_scores, true_labels, pred_labels = get_predictions(exp['model_state_dict'], extra_loader)
        experiment_results.append({
            n1: exp[n1],
            n2: exp[n2],
            "true_labels": true_labels,
            "pred_labels": pred_labels,
            "pred_scores": pred_scores
        })

        print(f"First 10 true labels:")
        [print(num, end=" ") for num in true_labels[:10]]
        print(f"...\n")

        print(f"First 10 pred labels:")
        [print(num, end=" ") for num in pred_labels[:10]]
        print(f"...\n")

        print(f"First 5 pred_scores:")
        [print(num, end=" ") for num in pred_scores[:5]]
        print(f"...\n")

        # del pred_scores, true_labels, pred_lables
        torch.cuda.empty_cache()
    return experiment_results

def plot_cm(experiment_results, hyper_param_names, n_rows=4, n_cols=4):
    fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols * 5, n_rows * 5))
    axes = axes.flatten()

    hparam_1, hparam_2 = hyper_param_names
    
    for i, exp_rs in enumerate(experiment_results):
        true_labels, pred_labels = exp_rs['true_labels'], exp_rs['pred_labels']
        cm = confusion_matrix(true_labels, pred_labels)
        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=range(0,10))
        disp.plot(ax=axes[i], cmap = plt.cm.Blues)
        axes[i].set_title(f"Exp {i+1}: {hparam_1}={exp_rs[hparam_1]}, {hparam_2}={exp_rs[hparam_2]}")
    
    plt.tight_layout()
    plt.show()

def plot_pr(experiment_results, hyper_param_names, n_rows=4, n_cols=4):
    fig, axes = plt.subplots(n_rows,n_cols, figsize=(n_cols * 5, n_rows * 5))
    axes = axes.flatten()

    hparam_1, hparam_2 = hyper_param_names
    accuracies = []
    f1_scores = []
    
    for i, exp_rs in enumerate(experiment_results):
        true_labels, pred_labels, pred_scores = exp_rs['true_labels'], exp_rs['pred_labels'], exp_rs['pred_scores']
        true_labels_bin, pred_labels_bin = label_binarize(true_labels, classes=range(0,10)), label_binarize(pred_labels, classes=range(0,10))
        
        accuracy, precision, recall, f1 = get_metrics(true_labels, pred_labels)
        accuracies.append(accuracy)
        f1_scores.append(f1)
        
        for j in range(0, 10):
            # print(f"Class {j}: Prec:{precision[j]:.2f}, Recall:{recall[j]:.2f}, F_1 Score:{f1[j]:.2f}")
            precision_i, recall_i, _ = precision_recall_curve(true_labels_bin[:, j], np.array(pred_scores)[:, j])
    
            average_precision = average_precision_score(true_labels_bin[:, j], np.array(pred_scores)[:, j])
            axes[i].step(recall_i, precision_i, where="post", label=f"Class {j} AP={average_precision:.2f}")
            axes[i].set_title(f"PR-Curve {hparam_1}={exp_rs[hparam_1]}, {hparam_2}={exp_rs[hparam_2]}")
        axes[i].legend()
        axes[i].set_xlabel("Recall")
        axes[i].set_ylabel("Precision")
    
    # for j in range(i+1, 16):
    #     fig.delaxes(axes[j])
    
    plt.tight_layout()
    plt.show()
    return accuracies, f1_scores

def plot_rocauc(experiment_results, hyper_param_names, curve_type, n_rows=4, n_cols=4):
    fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols * 5, n_rows * 5))
    axes = axes.flatten()

    hparam_1, hparam_2 = hyper_param_names

    for i, exp_rs in enumerate(experiment_results):
        true_labels, pred_scores = exp_rs['true_labels'], exp_rs['pred_scores']
        true_labels_bin = label_binarize(true_labels, classes=range(0, 10))

        # All Classes' ROC curve & ROC Area Under Curve
        fpr = dict()
        tpr = dict()
        roc_auc = dict()

        for j in range(10):
            fpr[j], tpr[j], _ = roc_curve(true_labels_bin[:, j], np.array(pred_scores)[:, j])
            roc_auc[j] = auc(fpr[j], tpr[j])

        # Macro-Average ROC & ROC-AUC
        all_fpr = np.unique(np.concatenate([fpr[j] for j in range(10)]))
        mean_tpr = np.zeros_like(all_fpr)
        for j in range(10):
            mean_tpr += np.interp(all_fpr, fpr[j], tpr[j])
        mean_tpr /= 10

        fpr["macro"] = all_fpr
        tpr["macro"] = mean_tpr
        roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

        # Compute micro-average ROC curve and ROC area
        fpr["micro"], tpr["micro"], _ = roc_curve(true_labels_bin.ravel(), np.array(pred_scores).ravel())
        roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

        # Plot only Macro or Micro ROC curves
        if curve_type == "macro_micro":
            axes[i].plot(fpr["macro"], tpr["macro"], label=f"Macro (AUC={roc_auc['macro']:.2f})")
            axes[i].plot(fpr["micro"], tpr["micro"], label=f"Micro (AUC={roc_auc['micro']:.2f})")
        elif curve_type == "all":
            # Plot all ROC curves
            for j in range(10):
                axes[i].plot(fpr[j], tpr[j], label=f"Class {j} (AUC={roc_auc[j]:.2f})")

        axes[i].plot([0, 1], [0, 1], "k--")
        axes[i].set_xlabel("False Positive Rate")
        axes[i].set_ylabel("True Positive Rate")
        axes[i].set_title(f"ROC Curve {i+1}, {hparam_1}={exp_rs[hparam_1]}, {hparam_2}={exp_rs[hparam_2]}")
        axes[i].legend(loc='lower right')

    plt.tight_layout()
    plt.show()

def split_train_valid(train_dataset, train_ratio):
    ori_len = len(train_dataset)
    train_size = int(train_ratio * ori_len)
    valid_size = ori_len - train_size
    train_dataset_, valid_dataset_ = random_split(train_dataset, [train_size, valid_size])
    return train_dataset_, valid_dataset_

# Universal Train Dataset without splitting
exp1_universal_train_dataset = SVHNDataset(mat_file=os.path.join(path_dataset,"train_32x32.mat"))
exp1_mean, exp1_std = exp1_universal_train_dataset.get_meanstd()

print(f"Channel Means: {exp1_mean}")
print(f"Channel Stds: {exp1_std}")

Channel Means: [0.4376845359802246, 0.4437684714794159, 0.47280389070510864]
Channel Stds: [0.19803018867969513, 0.2010156661272049, 0.19703581929206848]

exp1_hyperparams = {
    "num_epochs": 30,
    "lr": 1e-5,
    "criterion": nn.CrossEntropyLoss(),
    "transform": A.Compose([
        A.Normalize(mean=exp1_mean, std=exp1_std),
        ToTensorV2()
    ])
}

exp1_models = [SmallVGG().to(device) for _ in range(0,6)]

candidate_optimizers = [
    optim.Adam(exp1_models[0].parameters(), lr=exp1_hyperparams['lr']), 
    optim.SGD(exp1_models[1].parameters(), lr=exp1_hyperparams['lr'], momentum=0.9),
    optim.RMSprop(exp1_models[2].parameters(), lr=exp1_hyperparams['lr']),
    optim.AdamW(exp1_models[3].parameters(), lr=exp1_hyperparams['lr'], weight_decay=0.01),
    optim.Adagrad(exp1_models[4].parameters(), lr=exp1_hyperparams['lr']),
    optim.SGD(exp1_models[5].parameters(), lr=exp1_hyperparams['lr'], momentum=0.9, nesterov=True)]

# Train & Test Dataset
exp1_train_dataset = SVHNDataset(mat_file=os.path.join(path_dataset,"train_32x32.mat"), transform=exp1_hyperparams['transform'])
exp1_train_dataset, exp1_valid_dataset = split_train_valid(exp1_train_dataset, train_ratio=0.8)

# Test Dataset
exp1_test_dataset = SVHNDataset(mat_file=os.path.join(path_dataset,"test_32x32.mat"), transform=exp1_hyperparams['transform'])

print(f"Train Size:{exp1_train_dataset.__len__()}\nValidation Size:{exp1_valid_dataset.__len__()}\nTest Size:{exp1_test_dataset.__len__()}")

Train Size:58605
Validation Size:14652
Test Size:26032

# Data Loaders
exp1_train_loader = DataLoader(exp1_train_dataset, batch_size=128, shuffle=True)
exp1_valid_loader = DataLoader(exp1_valid_dataset, batch_size=128, shuffle=True)
exp1_test_loader = DataLoader(exp1_test_dataset, batch_size=128, shuffle=False)

def run_exp1(optimizers, models, hyper_params, train_loader, valid_loader):
    experiments = []
    for i, [optimizer, exp1_model] in enumerate(zip(optimizers, models)):
        print(f"Experiment {i+1}. Running experiment on optimizer: {optimizer.__class__.__name__}")

        criterion = hyper_params['criterion']
        num_epochs = hyper_params['num_epochs']
        train_losses, test_losses = train_and_evaluate(exp1_model, train_loader, valid_loader, criterion, optimizer, num_epochs)

        experiments.append({
            "optimizer": optimizer.__class__.__name__,
            "others":"same",
            "train_losses": train_losses,
            "test_losses": test_losses,
            "model_state_dict": exp1_model.state_dict()
        })

        del exp1_model, criterion, optimizer
        torch.cuda.empty_cache()

    return experiments

exp1 = run_exp1(candidate_optimizers, exp1_models, exp1_hyperparams, exp1_train_loader, exp1_valid_loader)
time_str = str(time.time()).replace(".","")
torch.save(exp1, f"./models/exp1_{time_str}.pth")

exp1_loaded = torch.load("./models/exp1_17302273106995156.pth")
exp1_results = get_experiment_results(exp1_loaded, test_hyperparam_names=["optimizer", "others"], extra_loader=exp1_test_loader)

plot_el(exp1_loaded, ["optimizer", "others"], n_rows=1, n_cols=6)

plot_cm(exp1_results, ["optimizer", "others"], n_rows=1, n_cols=6)

exp1_accuracies, exp1_f1s = plot_pr(exp1_results, ["optimizer", "others"], n_rows=1, n_cols=6)
print(f"Accuracies:")
for acc in exp1_accuracies:
    print(f"{acc:.3f}", end=", ")
print("\n")

print(f"F1 Score Lists:")
for f1 in exp1_f1s:
    for val in f1:
        print(f"{val:.3f}", end=", ")
    print(f"Avg F1={np.mean(f1):.3f}")

Accuracies:
0.755, 0.196, 0.687, 0.704, 0.196, 0.196, 

F1 Score Lists:
0.710, 0.861, 0.825, 0.688, 0.776, 0.694, 0.680, 0.800, 0.574, 0.639, Avg F1=0.725
0.000, 0.328, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, Avg F1=0.033
0.634, 0.816, 0.810, 0.573, 0.707, 0.628, 0.521, 0.741, 0.462, 0.573, Avg F1=0.646
0.624, 0.846, 0.756, 0.619, 0.761, 0.657, 0.589, 0.749, 0.536, 0.562, Avg F1=0.670
0.000, 0.328, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, Avg F1=0.033
0.000, 0.328, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, Avg F1=0.033

plot_rocauc(exp1_results, ["optimizer", "others"], curve_type="all", n_rows=1, n_cols=6)

plot_rocauc(exp1_results, ["optimizer", "others"], curve_type="macro_micro", n_rows=1, n_cols=6)

# Universal Train Dataset without splitting
exp2_universal_train_dataset = SVHNDataset(mat_file=os.path.join(path_dataset,"train_32x32.mat"))
exp2_mean, exp2_std = exp2_universal_train_dataset.get_meanstd()

print(f"Channel Means: {exp2_mean}")
print(f"Channel Stds: {exp2_std}")

Channel Means: [0.4376845359802246, 0.4437684714794159, 0.47280389070510864]
Channel Stds: [0.19803018867969513, 0.2010156661272049, 0.19703581929206848]

exp2_hyperparams = {
    "criterion": nn.CrossEntropyLoss(),
    "transform": A.Compose([
        A.Normalize(mean=exp2_mean, std=exp2_std),
        ToTensorV2()
    ]),
    "optimizer":optim.Adam,
}

candidate_epochs = [10, 15, 20, 25]
candidate_lr = [1e-3, 1e-4, 1e-5, 1e-6]

torch.cuda.empty_cache()

# Train & Validation Datasets
exp2_train_dataset = SVHNDataset(mat_file=os.path.join(path_dataset,"train_32x32.mat"), transform=exp2_hyperparams['transform'])
exp2_train_dataset, exp2_valid_dataset = split_train_valid(exp2_train_dataset, train_ratio=0.8)

# Test Dataset
exp2_test_dataset = SVHNDataset(mat_file=os.path.join(path_dataset,"test_32x32.mat"), transform=exp2_hyperparams['transform'])

print(f"Train Size:{exp2_train_dataset.__len__()}\nValidation Size:{exp2_valid_dataset.__len__()}\nTest Size:{exp2_test_dataset.__len__()}")

Train Size:58605
Validation Size:14652
Test Size:26032

exp2_train_loader = DataLoader(exp2_train_dataset, batch_size=128, shuffle=True)
exp2_valid_loader = DataLoader(exp2_valid_dataset, batch_size=128, shuffle=True)
exp2_test_loader = DataLoader(exp2_valid_dataset, batch_size=128, shuffle=False)

def run_exp2(epochs, lr_list, hyper_params, train_loader, test_loader):
    combinations = list(itertools.product(epochs, lr_list))
    experiments = []
    for i, combo in enumerate(combinations):
        num_epochs, lr = combo

        print(f"Running Exp {i+1}: num_epoch={num_epochs}, lr={lr}")
        this_model = SmallVGG().to(device)
        criterion = hyper_params['criterion']
        optimizer = hyper_params['optimizer'](this_model.parameters(), lr=lr)
        train_losses, test_losses = train_and_evaluate(this_model, train_loader, test_loader, criterion, optimizer, num_epochs)

        experiments.append({
            "num_epochs": num_epochs,
            "lr": lr,
            "train_losses": train_losses,
            "test_losses": test_losses,
            "model_state_dict": this_model.state_dict()
        })

        del this_model, criterion, optimizer
    return experiments

exp2 = run_exp2(candidate_epochs, candidate_lr, exp2_hyperparams, exp2_train_loader, exp2_valid_loader)
time_str = str(time.time()).replace(".","")
torch.save(exp2, f"./models/exp2_1_{time_str}.pth")

exp2_loaded = torch.load("./models/exp2_1_1730229238268616.pth")
exp2_results = get_experiment_results(exp2_loaded, test_hyperparam_names=["num_epochs", "lr"], extra_loader=exp2_test_loader)

plot_el(exp2_loaded, ["num_epochs", "lr"], n_rows=4, n_cols=4)

plot_cm(exp2_results, ["num_epochs", "lr"], n_rows=4, n_cols=4)

exp2_accuracies, exp2_f1s = plot_pr(exp2_results, ["num_epochs", "lr"], n_rows=4, n_cols=4)
print(f"Accuracies:")
for acc in exp2_accuracies:
    print(f"{acc:.3f}", end=", ")
print("\n")

print(f"F1 Score Lists:")
for f1 in exp2_f1s:
    for val in f1:
        print(f"{val:.3f}", end=", ")
    print(f"Avg F1={np.mean(f1):.3f}, Std={np.std(f1):.3f}")

Accuracies:
0.920, 0.846, 0.498, 0.187, 0.910, 0.859, 0.501, 0.187, 0.909, 0.890, 0.583, 0.187, 0.907, 0.884, 0.691, 0.187, 

F1 Score Lists:
0.924, 0.937, 0.941, 0.904, 0.934, 0.910, 0.886, 0.932, 0.892, 0.898, Avg F1=0.916, Std=0.019
0.849, 0.895, 0.909, 0.795, 0.862, 0.808, 0.796, 0.868, 0.750, 0.793, Avg F1=0.833, Std=0.049
0.399, 0.683, 0.581, 0.412, 0.533, 0.438, 0.369, 0.503, 0.118, 0.250, Avg F1=0.429, Std=0.154
0.000, 0.315, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, Avg F1=0.031, Std=0.094
0.911, 0.931, 0.938, 0.895, 0.917, 0.897, 0.886, 0.915, 0.864, 0.885, Avg F1=0.904, Std=0.022
0.876, 0.889, 0.911, 0.819, 0.873, 0.836, 0.823, 0.876, 0.770, 0.825, Avg F1=0.850, Std=0.040
0.333, 0.713, 0.555, 0.265, 0.587, 0.506, 0.336, 0.557, 0.057, 0.059, Avg F1=0.397, Std=0.213
0.000, 0.315, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, Avg F1=0.031, Std=0.094
0.919, 0.929, 0.935, 0.895, 0.919, 0.899, 0.874, 0.922, 0.842, 0.903, Avg F1=0.904, Std=0.027
0.903, 0.928, 0.931, 0.864, 0.904, 0.873, 0.849, 0.906, 0.793, 0.863, Avg F1=0.881, Std=0.040
0.470, 0.741, 0.680, 0.442, 0.695, 0.550, 0.473, 0.631, 0.091, 0.442, Avg F1=0.521, Std=0.179
0.000, 0.315, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, Avg F1=0.031, Std=0.094
0.918, 0.927, 0.934, 0.895, 0.921, 0.887, 0.869, 0.911, 0.868, 0.889, Avg F1=0.902, Std=0.023
0.895, 0.924, 0.925, 0.856, 0.893, 0.871, 0.842, 0.904, 0.796, 0.844, Avg F1=0.875, Std=0.039
0.658, 0.812, 0.768, 0.601, 0.742, 0.633, 0.569, 0.763, 0.452, 0.605, Avg F1=0.660, Std=0.105
0.000, 0.315, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, Avg F1=0.031, Std=0.094

plot_rocauc(exp2_results, ["num_epochs", "lr"], curve_type="all", n_rows=4, n_cols=4)

plot_rocauc(exp2_results, ["num_epochs", "lr"], curve_type="macro_micro", n_rows=4, n_cols=4)

exp2_2_hyperparams = {
    "num_epoch": 50,
    "criterion": nn.CrossEntropyLoss(),
    "transform": A.Compose([
        A.Normalize(mean=exp2_mean, std=exp2_std),
        ToTensorV2()
    ]),
    "optimizer":optim.Adam,
}

# More detailed candidate learning rates around 1e-4, that is 10e-4.
exp2_2_candidate_lr = [17e-4, 15e-4, 15e-4, 14e-4, 13e-4, 12e-4, 8e-4, 7e-4, 6e-4, 5e-4, 4e-4, 3e-4]

def run_exp2_2(lr_list, hyper_params, train_loader, test_loader):
    experiments = []
    for i, lr in enumerate(lr_list):

        print(f"Running Exp {i+1}: lr={lr}")
        this_model = SmallVGG().to(device)
        num_epochs = hyper_params['num_epoch']
        criterion = hyper_params['criterion']
        optimizer = hyper_params['optimizer'](this_model.parameters(), lr=lr)
        train_losses, test_losses = train_and_evaluate(this_model, train_loader, test_loader, criterion, optimizer, num_epochs)

        experiments.append({
            "num_epochs": num_epochs,
            "lr": lr,
            "train_losses": train_losses,
            "test_losses": test_losses,
            "model_state_dict": this_model.state_dict()
        })

        del this_model, criterion, optimizer
    return experiments

exp2_2 = run_exp2_2(exp2_2_candidate_lr, exp2_2_hyperparams, exp2_train_loader, exp2_valid_loader)
time_str = str(time.time()).replace(".", "")
torch.save(exp2_2, f"./models/exp2_2_{time_str}.pth")

exp2_2_loaded = torch.load("./models/exp2_2_17302741969577262.pth")
exp2_2_results = get_experiment_results(exp2_2_loaded, test_hyperparam_names=["num_epochs", "lr"], extra_loader=exp2_test_loader)

plot_el(exp2_2_loaded, ["num_epochs", "lr"], n_rows=2, n_cols=6)

plot_cm(exp2_2_results, ["num_epochs", "lr"], n_rows=2, n_cols=6)

exp2_2_accuracies, exp2_2_f1s = plot_pr(exp2_2_results, ["num_epochs", "lr"], n_rows=2, n_cols=6)
print(f"Accuracies:")
for acc in exp2_2_accuracies:
    print(f"{acc:.3f}", end=", ")
print("\n")

print(f"F1 Score Lists:")
for f1 in exp2_2_f1s:
    for val in f1:
        print(f"{val:.3f}", end=", ")
    print(f"Avg F1={np.mean(f1):.3f}, Std={np.std(f1):.3f}")

Accuracies:
0.907, 0.900, 0.905, 0.905, 0.914, 0.911, 0.906, 0.911, 0.907, 0.912, 0.909, 0.908, 

F1 Score Lists:
0.918, 0.930, 0.931, 0.892, 0.924, 0.901, 0.870, 0.913, 0.859, 0.878, Avg F1=0.902, Std=0.024
0.912, 0.925, 0.926, 0.877, 0.912, 0.898, 0.854, 0.914, 0.852, 0.879, Avg F1=0.895, Std=0.026
0.915, 0.923, 0.929, 0.890, 0.913, 0.894, 0.877, 0.911, 0.868, 0.887, Avg F1=0.900, Std=0.019
0.917, 0.925, 0.932, 0.883, 0.910, 0.898, 0.876, 0.918, 0.849, 0.893, Avg F1=0.900, Std=0.024
0.924, 0.932, 0.935, 0.904, 0.921, 0.905, 0.891, 0.918, 0.862, 0.893, Avg F1=0.909, Std=0.021
0.921, 0.930, 0.935, 0.904, 0.916, 0.909, 0.878, 0.912, 0.861, 0.895, Avg F1=0.906, Std=0.022
0.913, 0.927, 0.938, 0.885, 0.923, 0.893, 0.871, 0.907, 0.864, 0.877, Avg F1=0.900, Std=0.024
0.915, 0.928, 0.942, 0.897, 0.922, 0.899, 0.885, 0.914, 0.860, 0.890, Avg F1=0.905, Std=0.023
0.909, 0.928, 0.932, 0.884, 0.921, 0.906, 0.880, 0.917, 0.845, 0.892, Avg F1=0.901, Std=0.025
0.916, 0.930, 0.935, 0.896, 0.925, 0.896, 0.890, 0.909, 0.876, 0.902, Avg F1=0.907, Std=0.018
0.918, 0.928, 0.935, 0.885, 0.918, 0.902, 0.887, 0.918, 0.861, 0.894, Avg F1=0.905, Std=0.022
0.914, 0.931, 0.938, 0.887, 0.919, 0.897, 0.880, 0.916, 0.848, 0.889, Avg F1=0.902, Std=0.026

plot_rocauc(exp2_2_results, ["num_epochs", "lr"], curve_type="all", n_rows=2, n_cols=6)

plot_rocauc(exp2_2_results, ["num_epochs", "lr"], curve_type="macro_micro", n_rows=2, n_cols=6)

exp3_1_hyperparams = {
    "num_epochs": 15,
    "lr": 1e-3,
    "criterion": nn.CrossEntropyLoss(),
    "optimizer": optim.Adam
}

# Group 1
candidate_angles = [15, 30, 45, 60]
candidate_crops = [0.08, 0.24, 0.40, 0.60] # Left Boundary

exp3_train_dataset = SVHNDataset(mat_file=os.path.join(path_dataset, "train_32x32.mat"))
exp3_test_dataset = SVHNDataset(mat_file=os.path.join(path_dataset, "test_32x32.mat"))
exp3_extra_dataset = SVHNDataset(mat_file=os.path.join(path_dataset, "extra_32x32.mat"))

print(f"Train Size:{exp3_train_dataset.__len__()}\nTest Size:{exp3_test_dataset.__len__()}\nExtra Size:{exp3_extra_dataset.__len__()}")

Train Size:73257
Test Size:26032
Extra Size:531131

def run_exp3_1(angles, crops, hyper_params, train_dataset, test_dataset):
    combinations = list(itertools.product(angles, crops))
    experiments = []
    for i, combo in enumerate(combinations):
        angle, crop = combo
        
        print(f"Running Exp {i+1}: angles={angle}, crop={crop}")
        this_model = SmallVGG().to(device)
        num_epochs = hyper_params['num_epochs']
        lr = hyper_params['lr']
        criterion = hyper_params['criterion']
        optimizer = hyper_params['optimizer'](this_model.parameters(), lr=lr)

        this_transform = A.Compose([
            A.RandomResizedCrop(32, 32, scale=(crop, 1.0)),
            A.Rotate(limit=angle),
            A.Normalize(mean=norm_mean, std=norm_std),
            ToTensorV2()
        ])

        # Generate Dataset
        print(f"Exp {i+1}: Generating dataset from transform")
        train_dataset.transform = this_transform
        test_dataset.transform = this_transform
        
        train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
        test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)

        # Train Model
        train_losses, test_losses = train_and_evaluate(this_model, 
                                                       train_loader, 
                                                       test_loader, 
                                                       criterion, 
                                                       optimizer,
                                                       num_epochs)

        experiments.append({
            "angle": angle,
            "crop": crop,
            "train_losses": train_losses,
            "test_losses": test_losses,
            "model_state_dict": this_model.state_dict()
        })

        del this_model, criterion, optimizer
        del train_loader, test_loader
        torch.cuda.empty_cache()

    return experiments

exp3_1 = run_exp3_1(candidate_angles, candidate_crops, exp3_1_hyperparams, exp3_train_dataset, exp3_test_dataset)
time_str = str(time.time()).replace(".","")
torch.save(exp3_1, f"./models/exp3_1_{time_str}.pth")

Running Exp 1: angles=15, crop=0.08
Exp 1: Generating dataset from transform

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:17<00:00, 33.61it/s]

Epoch[1/15], Train Loss:261.5060, Test Loss:201.0278

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:17<00:00, 33.42it/s]

Epoch[2/15], Train Loss:170.1129, Test Loss:147.9401

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:17<00:00, 33.24it/s]

Epoch[3/15], Train Loss:139.7338, Test Loss:131.5334

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:16<00:00, 33.80it/s]

Epoch[4/15], Train Loss:127.6843, Test Loss:119.5422

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:17<00:00, 33.41it/s]

exp3_1_loaded = torch.load("./models/exp3_1_1730131987195526.pth")
exp3_1_results = get_experiment_results(exp3_1_loaded, test_hyperparam_names=["angle", "crop"], extra_loader=exp2_extra_loader)

D:\Temps\temp\ipykernel_68752\2438005985.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  exp3_1_loaded = torch.load("./models/exp3_1_1730131987195526.pth")
100%|████████████████████████████████████████████████████████████████████████████████| 235/235 [00:06<00:00, 34.38it/s]

First 10 true labels:
4 7 8 7 1 1 7 4 3 0 ...

First 10 pred labels:
4 1 8 7 1 7 7 7 3 7 ...

First 5 pred_scores:
[1.4064831077575946e-07, 0.00011279848695266992, 1.5159866961766966e-05, 1.7165482404379873e-06, 0.9998559951782227, 2.1228647710813675e-06, 1.3362932804739103e-06, 7.832702067389619e-06, 1.4053844097361434e-06, 1.5434172837558435e-06] [0.03180306404829025, 0.4185839295387268, 0.02551024965941906, 0.03235312178730965, 0.1896570324897766, 0.00957075972110033, 0.006793879438191652, 0.2431216537952423, 0.009060983546078205, 0.0335453525185585] [2.112846075874586e-08, 4.6940115794313897e-07, 1.3628233546114643e-06, 8.282203634735197e-05, 8.77164207935266e-09, 1.4058948636375135e-06, 2.3308601157623343e-05, 1.5347421822298202e-06, 0.9998881816864014, 8.895452197066334e-07] [3.6493094768275114e-08, 1.460717385270982e-06, 2.4321957425854634e-06, 1.2626055649889167e-06, 2.2122497966847732e-07, 4.538042830404265e-09, 4.3692324425137485e-07, 0.9999939203262329, 5.015929716023493e-08, 3.6751121257339037e-08] [0.018730266019701958, 0.7045648694038391, 0.00017669936642050743, 0.0015132176922634244, 0.0020648883655667305, 7.167350850068033e-05, 9.595266601536423e-05, 0.2720440924167633, 0.0005919582326896489, 0.00014636351261287928] ...

100%|████████████████████████████████████████████████████████████████████████████████| 235/235 [00:06<00:00, 34.36it/s]

First 10 true labels:
4 7 8 7 1 1 7 4 3 0 ...

First 10 pred labels:
4 7 8 4 1 7 1 7 3 0 ...

First 5 pred_scores:
[2.0182224034215324e-05, 0.005355987697839737, 0.0007133720209822059, 0.0003770111652556807, 0.9903033375740051, 5.057433736510575e-05, 3.956997170462273e-05, 0.003078151261433959, 1.4097527127887588e-05, 4.7732180973980576e-05] [0.02147713303565979, 0.3031976819038391, 0.053317200392484665, 0.049746885895729065, 0.03985697776079178, 0.023932544514536858, 0.08461983501911163, 0.37980082631111145, 0.03162527456879616, 0.012425631284713745] [2.043546010099817e-05, 7.556287164334208e-05, 0.0016411993419751525, 0.00032324803760275245, 4.3842073864652775e-06, 4.808251833310351e-05, 0.00015477798297069967, 1.0236520211037714e-05, 0.9973805546760559, 0.00034151485306210816] [0.00569797633215785, 0.12763331830501556, 0.0003282544494140893, 0.0019110975554212928, 0.7916438579559326, 0.00014230738452170044, 0.024179695174098015, 0.046430524438619614, 0.0014189337380230427, 0.0006140433833934367] [0.0009190419805236161, 0.9700970649719238, 0.0002922282146755606, 0.00023085846623871475, 0.010859143920242786, 8.526008605258539e-05, 3.0573060939786956e-05, 0.017066553235054016, 4.434372385730967e-05, 0.0003748943272512406] ...

100%|████████████████████████████████████████████████████████████████████████████████| 235/235 [00:06<00:00, 34.97it/s]

First 10 true labels:
4 7 8 7 1 1 7 4 3 0 ...

First 10 pred labels:
4 1 8 7 1 1 1 1 3 0 ...

First 5 pred_scores:
[5.525651886273408e-06, 0.003244614228606224, 0.0006503831245936453, 5.366866025724448e-05, 0.9957312941551208, 4.878462277702056e-05, 9.872703230939806e-05, 3.6178047594148666e-05, 0.00011152042861795053, 1.9343691747053526e-05] [0.01701025292277336, 0.4711743891239166, 0.0068757557310163975, 0.0065680695697665215, 0.01812058500945568, 0.001882858807221055, 0.0036758719943463802, 0.46403390169143677, 0.0017211936647072434, 0.008937081322073936] [0.016106002032756805, 0.0009662715019658208, 0.04833371937274933, 0.00842353142797947, 0.005780481733381748, 0.039252474904060364, 0.053723789751529694, 0.00040336622623726726, 0.5178942680358887, 0.3091161251068115] [3.204526421995979e-08, 0.0062753139063715935, 1.4809371350565925e-05, 1.7198237401316874e-05, 5.7432793255429715e-05, 1.1583284731386811e-07, 1.5591742339893244e-05, 0.9936178922653198, 4.7079484488676826e-07, 1.1559553740880801e-06] [0.00037273240741342306, 0.9900857210159302, 2.1628995455102995e-05, 0.0003162486827932298, 0.00042396553908474743, 0.0002078549878206104, 0.0002391721063759178, 0.008045517839491367, 0.0002759082999546081, 1.137711024057353e-05] ...

100%|████████████████████████████████████████████████████████████████████████████████| 235/235 [00:06<00:00, 36.81it/s]

First 10 true labels:
4 7 8 7 1 1 7 4 3 0 ...

First 10 pred labels:
4 1 8 7 1 7 1 1 2 0 ...

First 5 pred_scores:
[2.9765080853394466e-06, 0.0005042628035880625, 0.0030127926729619503, 7.534459291491657e-05, 0.9960286617279053, 6.1026683397358283e-05, 0.00020444155961740762, 8.515617082593963e-05, 8.772012733970769e-06, 1.653061553952284e-05] [0.033330705016851425, 0.49084827303886414, 0.029589412733912468, 0.03180508688092232, 0.002717816736549139, 0.0004189242608845234, 0.007390279322862625, 0.31179216504096985, 0.09107621759176254, 0.0010310987709090114] [6.02355066803284e-06, 0.0006331368931569159, 0.004683933686465025, 0.000261747365584597, 1.727759990899358e-05, 1.0070411008200608e-05, 0.0003484832704998553, 0.00017784489318728447, 0.9937586784362793, 0.00010274533269694075] [0.0024907132610678673, 0.004774706903845072, 2.4819670215947554e-05, 0.00017629892681725323, 0.0003953950945287943, 6.228317488421453e-06, 0.0453050434589386, 0.946723222732544, 4.8757447075331584e-05, 5.4831594752613455e-05] [1.221009915752802e-05, 0.9993798732757568, 4.121716301597189e-06, 8.197375427698717e-05, 0.00018101245223078877, 6.163662328617647e-05, 0.00010631871555233374, 0.00014360524073708802, 2.67024188360665e-05, 2.5714155071909772e-06] ...

100%|████████████████████████████████████████████████████████████████████████████████| 235/235 [00:06<00:00, 33.91it/s]

First 10 true labels:
4 7 8 7 1 1 7 4 3 0 ...

First 10 pred labels:
4 1 8 7 7 1 7 4 3 1 ...

First 5 pred_scores:
[0.00014887879660818726, 0.020086141303181648, 0.011565621942281723, 0.003160470863804221, 0.9609534740447998, 0.0010658403625711799, 0.00040785325109027326, 0.002180159091949463, 0.00010687166650313884, 0.0003246604755986482] [0.003914410714060068, 0.4775712490081787, 0.1502229869365692, 0.020113371312618256, 0.018521776422858238, 0.01615132763981819, 0.016511449590325356, 0.25544029474258423, 0.026133766397833824, 0.015419360250234604] [0.05729537084698677, 0.0029975799843668938, 0.02326829545199871, 0.008132182992994785, 0.0016669145552441478, 0.004738755989819765, 0.11119315773248672, 0.0024263006635010242, 0.6793326139450073, 0.10894875228404999] [3.629895273249417e-09, 1.0017990916821873e-06, 6.597811630371098e-09, 4.013194221386129e-09, 4.4059028425635915e-08, 5.019764118263659e-12, 2.4533416964622745e-10, 0.9999990463256836, 3.317964306082111e-11, 2.7112856404443164e-10] [1.1688735867210198e-05, 0.08758752048015594, 6.902387667651055e-06, 3.2633583032293245e-05, 0.0011748791439458728, 8.984567330116988e-07, 6.587982170458417e-06, 0.9111714363098145, 4.463134700927185e-06, 3.0956537102611037e-06] ...

plot_el(exp3_1_loaded, ["angle", "crop"], n_rows=4, n_cols=4)

plot_cm(exp3_1_results, ["angle", "crop"], n_rows=4, n_cols=4)

exp1_accuracies, exp1_f1s = plot_pr(exp3_1_results, ["angle", "crop"], n_rows=4, n_cols=4)
print(f"Accuracies:")
for acc in exp1_accuracies:
    print(f"{acc:.3f}", end=", ")
print("\n")

print(f"F1 Score Lists:")
for f1 in exp1_f1s:
    for val in f1:
        print(f"{val:.3f}", end=", ")
    print(f"Avg F1={np.mean(f1):.3f}")

Accuracies:
0.747, 0.750, 0.719, 0.675, 0.762, 0.763, 0.736, 0.675, 0.766, 0.759, 0.723, 0.672, 0.753, 0.746, 0.730, 0.662, 

F1 Score Lists:
0.773, 0.772, 0.783, 0.732, 0.747, 0.729, 0.726, 0.693, 0.729, 0.737, Avg F1=0.742
0.751, 0.757, 0.776, 0.751, 0.725, 0.775, 0.734, 0.703, 0.741, 0.742, Avg F1=0.746
0.721, 0.723, 0.755, 0.730, 0.674, 0.738, 0.701, 0.695, 0.713, 0.703, Avg F1=0.715
0.678, 0.673, 0.715, 0.687, 0.625, 0.713, 0.648, 0.645, 0.671, 0.676, Avg F1=0.673
0.763, 0.769, 0.783, 0.767, 0.749, 0.792, 0.739, 0.744, 0.741, 0.727, Avg F1=0.758
0.764, 0.757, 0.799, 0.772, 0.755, 0.783, 0.744, 0.747, 0.751, 0.728, Avg F1=0.760
0.724, 0.708, 0.775, 0.747, 0.701, 0.782, 0.740, 0.723, 0.741, 0.737, Avg F1=0.738
0.678, 0.661, 0.689, 0.698, 0.654, 0.709, 0.665, 0.651, 0.682, 0.667, Avg F1=0.675
0.782, 0.787, 0.779, 0.756, 0.748, 0.776, 0.716, 0.764, 0.758, 0.750, Avg F1=0.762
0.770, 0.748, 0.794, 0.763, 0.735, 0.788, 0.731, 0.745, 0.743, 0.757, Avg F1=0.757
0.736, 0.706, 0.767, 0.736, 0.684, 0.747, 0.688, 0.710, 0.720, 0.747, Avg F1=0.724
0.668, 0.648, 0.713, 0.680, 0.626, 0.720, 0.664, 0.675, 0.676, 0.680, Avg F1=0.675
0.762, 0.763, 0.779, 0.738, 0.738, 0.763, 0.738, 0.748, 0.724, 0.740, Avg F1=0.749
0.746, 0.733, 0.788, 0.750, 0.716, 0.779, 0.724, 0.725, 0.754, 0.734, Avg F1=0.745
0.726, 0.712, 0.766, 0.755, 0.699, 0.752, 0.711, 0.711, 0.742, 0.733, Avg F1=0.731
0.645, 0.641, 0.708, 0.684, 0.610, 0.700, 0.651, 0.660, 0.665, 0.656, Avg F1=0.662

plot_rocauc(exp3_1_results, ["angle", "crop"], curve_type="all", n_rows=4, n_cols=4)

plot_rocauc(exp3_1_results, ["angle", "crop"], curve_type="macro_micro", n_rows=4, n_cols=4)

exp3_2_hyperparams = {
    "num_epoch": 15,
    "lr": 1e-3,
    "criterion": nn.CrossEntropyLoss(),
    "optimizer": optim.Adam,
    "crop": 0.08,
    "angle": 45
}

# Group 2
candidate_ratios = [0.25, 0.42, 0.58, 0.75]
candidate_channel_biases = [0, 32, 64, 128]

TA_norm_mean: List[float] = [0.4377, 0.4438, 0.4728]
"""norm_mean prepared by TA of CISC-3024"""

TA_norm_std: List[float] = [0.1980, 0.2010, 0.1970]
"""norm_std prepared by TA of CISC-3024"""

FULL_BIAS_norm_mean: Tuple[List[float], List[float], List[float], List[float]] = (
    [0.43011287, 0.42947713, 0.44553235], [0.482172, 0.47879672, 0.49193108],
    [0.5299231, 0.52622163, 0.5367366], [0.57701516, 0.57407594, 0.5768523])
"""norm_mean of all pixel values in 3 mat files in folder "SVHN_mat" """

FULL_BIAS_norm_std: Tuple[List[float], List[float], List[float], List[float]] = (
    [0.1968019, 0.19883512, 0.19997141], [0.19747807, 0.19839704, 0.1997657],
    [0.2061324, 0.20671913, 0.20880404], [0.2491606, 0.24821031, 0.2517171])
"""norm_std of all pixel values in 3 mat files in folder "SVHN_mat" """

TRAIN_BIAS_norm_mean: Tuple[List[float], List[float], List[float], List[float]] = (
    [0.4359728, 0.4420371, 0.47095722], [0.48945808, 0.49221805, 0.51708484],
    [0.5346211, 0.53527725, 0.55649185], [0.25027722, 0.25132284, 0.25705674])
"""norm_mean of only "train_32x32.mat" """

TRAIN_BIAS_norm_std: Tuple[List[float], List[float], List[float], List[float]] = (
    [0.19725639, 0.20023046, 0.1962663], [0.19868314, 0.20101954, 0.19824031],
    [0.207664, 0.20977464, 0.20946072], [0.5766456, 0.5753719, 0.5847802])
"""norm_std of only "train_32x32.mat" """

class AddBiasTransform:
    def __init__(self, bias: Union[int, Tuple[int, int]]) -> None:
        if isinstance(bias, tuple):
            self.bias1 = bias[0]
            self.bias2 = bias[1]
        else:
            self.bias1 = 0
            self.bias2 = bias

    def __call__(self, img: np.ndarray) -> np.ndarray:
        _dtype = img.dtype
        bias_value = random.randint(self.bias1, self.bias2)
        img = (img.astype(np.int16) + bias_value) % 256
        return img.astype(_dtype)

def run_exp3_2(ratios, biases, hyper_params, train_dataset, test_dataset):
    combinations = list(itertools.product(ratios, biases))
    experiments = []
    for i, combo in enumerate(combinations):
        ratio, bias = combo

        print(f"Running Exp {i+1}: ratio={ratio}, bias={bias}")
        this_model = SmallVGG().to(device)
        num_epochs = hyper_params['num_epoch']
        lr = hyper_params['lr']
        criterion = hyper_params['criterion']
        optimizer = hyper_params['optimizer'](this_model.parameters(), lr=lr)

        this_transform = A.Compose([
            A.Lambda(image=lambda img, **kwargs: AddBiasTransform(bias)(img)), # Lambda customized transform block
            A.RandomResizedCrop(32, 32, scale=(hyper_params['crop'], 1.0), ratio=(ratio, (1.0 / ratio))),
            A.Rotate(limit=hyper_params['angle']),
            A.Normalize(meand=FULL_BIAS_norm_mean[i % 4], std=FULL_BIAS_norm_std[i % 4]),
            ToTensorV2()
        ])

        train_dataset.transform = this_transform
        test_dataset.transform = this_transform

        train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
        test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)

        train_losses, test_losses = train_and_evaluate(this_model, 
                                                       train_loader, 
                                                       test_loader, 
                                                       criterion,
                                                       optimizer, 
                                                       num_epochs)

        experiments.append({
            "ratio": ratio,
            "bias": bias,
            "train_losses": train_losses,
            "test_losses": test_losses,
            "model_state_dict": this_model.state_dict(),
        })

        del this_model, criterion, optimizer
        del train_loader, test_loader, this_transform
        torch.cuda.empty_cache()
        
    return experiments

exp3_2 = run_exp3_2(candidate_ratios, candidate_channel_biases, exp3_2_hyperparams, exp3_train_dataset, exp3_test_dataset)
time_str = str(time.time()).replace(".", "")
torch.save(exp3_2, f"./models/exp3_2_{time_str}.pth")

D:\Temps\temp\ipykernel_91216\2610225581.py:15: UserWarning: Using lambda is incompatible with multiprocessing. Consider using regular functions or partial().
  A.Lambda(image=lambda img, **kwargs: AddBiasTransform(bias)(img)), # Lambda customized transform block
D:\Temps\temp\ipykernel_91216\2610225581.py:18: UserWarning: Argument 'meand' is not valid and will be ignored.
  A.Normalize(meand=FULL_BIAS_norm_mean[i % 4], std=FULL_BIAS_norm_std[i % 4]),

Running Exp 1: ratio=0.25, bias=0

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:17<00:00, 32.47it/s]

Epoch[1/15], Train Loss:285.6010, Test Loss:279.3851

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:14<00:00, 38.39it/s]

Epoch[2/15], Train Loss:263.1288, Test Loss:239.4954

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:16<00:00, 34.82it/s]

Epoch[3/15], Train Loss:227.2084, Test Loss:217.0606

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:16<00:00, 34.75it/s]

Epoch[4/15], Train Loss:208.5853, Test Loss:198.5745

exp3_2_loaded = torch.load("./models/exp3_2_17302004056334803.pth")
exp3_2_loaded
# exp3_2_results = get_experiment_results(exp3_2_loaded, test_hyperparam_names=["ratio", "bias"], extra_loader=exp3_extra_loader)

D:\Temps\temp\ipykernel_91216\193238723.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  exp3_2_loaded = torch.load("./models/exp3_2_17302004056334803.pth")

[{'ratio': 0.25,
  'bias': 0,
  'train_losses': [285.60101888067436,
   263.1288438009758,
   227.20843028959803,
   208.58533259586514,
   199.0652768724252,
   192.60961242600052,
   187.62277014550946,
   184.79933610886178,
   181.98672103465748,
   180.13460894030428,
   177.51193681000416,
   175.3553095402726,
   174.15974689944667,
   172.78727854130364,
   171.80259385200486],
  'test_losses': [279.38514354182223,
   239.49537703570198,
   217.060605208079,
   198.57451341666427,
   191.19259344362746,
   189.26621292151657,
   181.03300117043887,
   178.29486753426346,
   178.42596708559523,
   173.9601492507785,
   173.50485614701813,
   170.50235975489898,
   169.74062514772604,
   167.5622280999726,
   168.54016506905648],
  'model_state_dict': OrderedDict([('conv_layers.0.weight',
                tensor([[[[-0.1523, -0.0773, -0.1129],
                          [-0.2663,  0.1314, -0.0533],
                          [ 0.1193,  0.2347,  0.1989]],
                
                         [[-0.0536, -0.1561, -0.1747],
                          [ 0.1127,  0.0797, -0.0535],
                          [ 0.1120,  0.3193, -0.0161]],
                
                         [[-0.0247, -0.1091, -0.1827],
                          [-0.2097,  0.1656, -0.1122],
                          [ 0.0194,  0.0614,  0.2655]]],
                
                
                        [[[ 0.3277,  0.1230, -0.2185],
                          [-0.0154, -0.1962,  0.0602],
                          [ 0.0713, -0.1752,  0.0303]],
                
                         [[ 0.2652,  0.0770, -0.2367],
                          [ 0.0680, -0.0019, -0.0602],
                          [ 0.2056, -0.1793, -0.2694]],
                
                         [[ 0.1172,  0.0250, -0.1064],
                          [ 0.1801, -0.0639, -0.0893],
                          [ 0.0472, -0.0333,  0.0848]]],
                
                
                        [[[ 0.0949,  0.2757,  0.0202],
                          [ 0.0330,  0.0178, -0.0251],
                          [-0.1647, -0.1729, -0.1270]],
                
                         [[ 0.0774,  0.0433,  0.1939],
                          [ 0.1827,  0.0299,  0.0470],
                          [-0.2656, -0.1621, -0.1767]],
                
                         [[ 0.0956,  0.0394,  0.2411],
                          [-0.1004, -0.0902,  0.0946],
                          [ 0.0721,  0.0234, -0.2985]]],
                
                
                        [[[-0.1541, -0.0787, -0.2243],
                          [ 0.1071, -0.1443, -0.0635],
                          [ 0.3145, -0.0667,  0.2359]],
                
                         [[-0.0971, -0.0966, -0.2268],
                          [-0.0179,  0.0389, -0.2239],
                          [ 0.1591,  0.2411,  0.1385]],
                
                         [[ 0.0799,  0.0823, -0.0626],
                          [ 0.1028, -0.0625, -0.1571],
                          [ 0.0919,  0.1127, -0.0068]]],
                
                
                        [[[ 0.0088,  0.1103, -0.1297],
                          [ 0.1055, -0.0917, -0.1807],
                          [-0.1220,  0.1195,  0.1939]],
                
                         [[ 0.1591, -0.0074, -0.0441],
                          [-0.1843, -0.2587, -0.0953],
                          [-0.0346, -0.0670,  0.0437]],
                
                         [[ 0.1174,  0.0307,  0.1369],
                          [ 0.0595,  0.0052, -0.0693],
                          [-0.1894,  0.0671, -0.1880]]],
                
                
                        [[[ 0.0188,  0.0650, -0.1495],
                          [ 0.1468,  0.2043, -0.2921],
                          [ 0.1070, -0.0283, -0.0285]],
                
                         [[ 0.1268,  0.0767, -0.2025],
                          [ 0.1501, -0.1676,  0.0042],
                          [ 0.2777,  0.0504, -0.1909]],
                
                         [[-0.1776,  0.0966, -0.2976],
                          [ 0.1752,  0.0795,  0.0257],
                          [ 0.1410,  0.0879, -0.2465]]],
                
                
                        [[[ 0.0377,  0.0185, -0.0334],
                          [-0.1330, -0.0027, -0.1204],
                          [ 0.0908,  0.1117, -0.0289]],
                
                         [[-0.3226, -0.2324,  0.0939],
                          [-0.0787, -0.2005,  0.0371],
                          [ 0.0884,  0.0173, -0.0998]],
                
                         [[ 0.0282, -0.1353,  0.2034],
                          [-0.1141,  0.0665,  0.1155],
                          [ 0.0825,  0.2154,  0.1297]]],
                
                
                        [[[ 0.1175,  0.2004,  0.0609],
                          [ 0.0233,  0.0301,  0.0506],
                          [ 0.0100, -0.1515, -0.2276]],
                
                         [[ 0.3286, -0.0232, -0.0705],
                          [-0.0622, -0.1094,  0.0998],
                          [ 0.0425, -0.1187,  0.0606]],
                
                         [[ 0.1025, -0.0736,  0.0303],
                          [ 0.1361, -0.0239, -0.0987],
                          [-0.0500, -0.0485, -0.2305]]]], device='cuda:0')),
               ('conv_layers.0.bias',
                tensor([ 0.2275,  0.1235,  0.1745,  0.2452, -0.0546,  0.2130, -0.3062,  0.2186],
                       device='cuda:0')),
               ('conv_layers.2.weight',
                tensor([[[[-0.0620, -0.1182,  0.0978],
                          [-0.0866, -0.0096,  0.0034],
                          [-0.0678, -0.1468, -0.0922]],
                
                         [[-0.0551, -0.0926, -0.0847],
                          [ 0.0156,  0.1055, -0.0151],
                          [ 0.0709, -0.1050, -0.0483]],
                
                         [[-0.1333, -0.0867,  0.0725],
                          [ 0.0447, -0.1031, -0.0592],
                          [ 0.0698, -0.0762, -0.1677]],
                
                         ...,
                
                         [[-0.0866,  0.0030,  0.0082],
                          [-0.1262, -0.0993,  0.0552],
                          [-0.1184, -0.1057, -0.0337]],
                
                         [[-0.0585,  0.0348, -0.0963],
                          [-0.0393,  0.0843, -0.0527],
                          [ 0.0481, -0.1376, -0.0291]],
                
                         [[ 0.0240,  0.0395, -0.0063],
                          [-0.1284, -0.1333,  0.0151],
                          [ 0.0454,  0.0034,  0.0443]]],
                
                
                        [[[ 0.0531, -0.0526, -0.0537],
                          [-0.0647, -0.0128, -0.0721],
                          [ 0.1630, -0.0359,  0.0812]],
                
                         [[ 0.0149,  0.2055,  0.1578],
                          [ 0.0497,  0.1754,  0.0323],
                          [ 0.0703, -0.0536,  0.0590]],
                
                         [[-0.0620, -0.0210,  0.0264],
                          [-0.1904, -0.1783, -0.0548],
                          [ 0.0673, -0.0651,  0.0041]],
                
                         ...,
                
                         [[ 0.1631,  0.0059,  0.1200],
                          [ 0.1638,  0.0462, -0.0726],
                          [ 0.0534,  0.0727, -0.0588]],
                
                         [[-0.0232, -0.0012, -0.0633],
                          [-0.0790, -0.0332,  0.0123],
                          [-0.1287, -0.0758,  0.1302]],
                
                         [[-0.0058,  0.1056, -0.1245],
                          [ 0.1252,  0.0183, -0.0710],
                          [ 0.0661,  0.0654, -0.0861]]],
                
                
                        [[[-0.2948, -0.0618,  0.1557],
                          [-0.0112,  0.0559,  0.2870],
                          [-0.3797,  0.0139, -0.0121]],
                
                         [[-0.0912,  0.0721, -0.1241],
                          [ 0.1783,  0.1585, -0.1194],
                          [-0.2620,  0.1524, -0.1744]],
                
                         [[-0.1084, -0.1480, -0.1109],
                          [ 0.1240, -0.0301, -0.0901],
                          [ 0.0327,  0.0604,  0.0452]],
                
                         ...,
                
                         [[-0.1587, -0.1341,  0.0158],
                          [-0.0132,  0.1977,  0.1563],
                          [-0.4267,  0.0588, -0.1386]],
                
                         [[-0.0119, -0.1043, -0.0680],
                          [-0.0032, -0.1911, -0.1774],
                          [ 0.2749, -0.0752, -0.2279]],
                
                         [[ 0.0608,  0.1326, -0.1098],
                          [ 0.0197,  0.1264,  0.0327],
                          [-0.1633,  0.1057,  0.0215]]],
                
                
                        ...,
                
                
                        [[[ 0.0368,  0.0533, -0.1047],
                          [ 0.0444, -0.0294, -0.1479],
                          [-0.0259, -0.0150, -0.0891]],
                
                         [[-0.2848, -0.0623, -0.0020],
                          [-0.0533,  0.1008,  0.0540],
                          [ 0.0016,  0.1140,  0.1449]],
                
                         [[ 0.0886,  0.0865,  0.1307],
                          [-0.0143,  0.0884,  0.1310],
                          [ 0.1458,  0.0720,  0.1264]],
                
                         ...,
                
                         [[-0.0722, -0.0384,  0.1474],
                          [-0.2310, -0.1018,  0.0049],
                          [-0.0676, -0.1545, -0.0324]],
                
                         [[-0.0358, -0.1312, -0.1859],
                          [ 0.0976, -0.1062, -0.1038],
                          [ 0.0914, -0.0124,  0.0264]],
                
                         [[-0.1615,  0.0458,  0.2021],
                          [ 0.0081, -0.0857,  0.0783],
                          [ 0.0428,  0.0338,  0.0381]]],
                
                
                        [[[-0.1266, -0.2603, -0.0201],
                          [-0.1353, -0.1716,  0.1380],
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                         [[ 0.2844,  0.2588,  0.1256],
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                         ...,
                
                         [[-0.1031, -0.1013, -0.0284],
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                          [-0.0058,  0.0107, -0.2370]],
                
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                         [[ 0.1058, -0.1021, -0.1506],
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                          [-0.0522, -0.1077, -0.0077]]],
                
                
                        [[[ 0.0400, -0.0473, -0.1268],
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                         [[-0.3007, -0.3178, -0.2019],
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                          [ 0.1860,  0.1410, -0.0062]],
                
                         ...,
                
                         [[-0.0503,  0.0448, -0.0063],
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                          [ 0.1004,  0.0598, -0.0272]],
                
                         [[-0.0394, -0.1373,  0.0507],
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                          [ 0.0924, -0.0820, -0.1255]],
                
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                          [ 0.0518, -0.0865, -0.0005]]]], device='cuda:0')),
               ('conv_layers.2.bias',
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                       device='cuda:0')),
               ('conv_layers.5.weight',
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                          [ 0.0366,  0.0069,  0.0614],
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                         [[ 0.0886, -0.0404,  0.1289],
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                         [[ 0.1394,  0.1503, -0.0332],
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                          [ 0.1016,  0.1918,  0.0435]],
                
                         ...,
                
                         [[ 0.3824,  0.0123, -0.0073],
                          [-0.0401, -0.1085, -0.0083],
                          [-0.1506, -0.1128,  0.0526]],
                
                         [[-0.1370,  0.0116, -0.1983],
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                         [[ 0.0591, -0.0100,  0.0550],
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                          [ 0.0645,  0.2001, -0.0133]]],
                
                
                        [[[ 0.0166,  0.0461, -0.0505],
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                          [ 0.0351,  0.0281,  0.0605]],
                
                         [[-0.0907,  0.0165,  0.0238],
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                          [-0.0343, -0.0466, -0.0190]],
                
                         [[-0.0702, -0.0585,  0.0180],
                          [-0.0718, -0.1076, -0.1010],
                          [ 0.0093, -0.0628, -0.0775]],
                
                         ...,
                
                         [[ 0.0317, -0.0573, -0.0370],
                          [ 0.0198, -0.0051,  0.0404],
                          [ 0.0331, -0.1162,  0.0191]],
                
                         [[ 0.0240,  0.0318, -0.0707],
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                          [-0.0822,  0.0311,  0.0430]],
                
                         [[-0.0038, -0.0178,  0.0116],
                          [ 0.0412, -0.0458, -0.0588],
                          [ 0.0359,  0.0427, -0.0890]]],
                
                
                        [[[ 0.0300,  0.0294, -0.0736],
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                          [-0.0097, -0.0137,  0.0304]],
                
                         [[ 0.0421, -0.2128, -0.0819],
                          [ 0.0516, -0.1340, -0.0134],
                          [ 0.0782,  0.0793,  0.1028]],
                
                         [[ 0.1058,  0.2718,  0.0932],
                          [ 0.0787,  0.3077,  0.1951],
                          [-0.1103, -0.0378, -0.2109]],
                
                         ...,
                
                         [[-0.1820, -0.1675, -0.0804],
                          [-0.1062, -0.0467, -0.1129],
                          [-0.0306, -0.1130, -0.0821]],
                
                         [[ 0.0688, -0.0984, -0.0152],
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                          [-0.0935, -0.0662, -0.1653]],
                
                         [[-0.0873, -0.0251, -0.1141],
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                        ...,
                
                
                        [[[ 0.0613, -0.0488,  0.0241],
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                         [[-0.0988, -0.0465, -0.1394],
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                          [-0.1583, -0.0166, -0.1195]],
                
                         [[-0.3096, -0.1492, -0.3836],
                          [ 0.0084,  0.2377,  0.0267],
                          [-0.0557,  0.0941, -0.1131]],
                
                         ...,
                
                         [[ 0.0751,  0.0750, -0.1014],
                          [ 0.0969,  0.0852, -0.0560],
                          [ 0.0796,  0.1299,  0.0184]],
                
                         [[-0.1766, -0.2031, -0.2026],
                          [-0.0197,  0.0531,  0.0390],
                          [ 0.0712,  0.1893, -0.0487]],
                
                         [[-0.0403,  0.0031, -0.1264],
                          [ 0.1276,  0.2123,  0.1120],
                          [ 0.0622,  0.0779, -0.0498]]],
                
                
                        [[[ 0.0070, -0.0718,  0.0293],
                          [-0.0840, -0.0723,  0.0687],
                          [-0.0510,  0.0560,  0.0107]],
                
                         [[ 0.0720, -0.1333, -0.0879],
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                          [ 0.0662, -0.0986, -0.0078]],
                
                         [[-0.0678,  0.1677, -0.0685],
                          [-0.1904,  0.1986,  0.0049],
                          [-0.1344,  0.1621, -0.0782]],
                
                         ...,
                
                         [[-0.0847, -0.0239, -0.0035],
                          [-0.1917, -0.1198, -0.0470],
                          [-0.0932,  0.0813,  0.1540]],
                
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                          [ 0.0368,  0.1589,  0.0334]],
                
                         [[-0.0587,  0.0486, -0.0553],
                          [-0.1535,  0.1623,  0.1503],
                          [-0.2253,  0.1490,  0.2127]]],
                
                
                        [[[ 0.0219, -0.0703, -0.0722],
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                          [ 0.0758,  0.0591,  0.0258]],
                
                         [[ 0.1017,  0.0086, -0.0928],
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                          [-0.1187,  0.0390,  0.0427]],
                
                         [[-0.0650, -0.0480, -0.0121],
                          [-0.1027,  0.0808,  0.1016],
                          [ 0.0319, -0.0092,  0.0996]],
                
                         ...,
                
                         [[-0.0615, -0.1215, -0.2080],
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                          [-0.2299, -0.1294, -0.1091]],
                
                         [[-0.0122, -0.0628,  0.0094],
                          [-0.0478, -0.1228,  0.0127],
                          [ 0.0567,  0.0763,  0.0761]],
                
                         [[ 0.0241,  0.0190,  0.1096],
                          [-0.0824,  0.0593,  0.0552],
                          [-0.1277, -0.1938, -0.0822]]]], device='cuda:0')),
               ('conv_layers.5.bias',
                tensor([ 0.2661, -0.0829,  0.2653,  0.1021,  0.0145, -0.0844, -0.0671,  0.0590,
                        -0.2695,  0.1640, -0.1131,  0.0048,  0.0494, -0.0116, -0.0065, -0.1071,
                         0.3102,  0.1289, -0.0744,  0.0406,  0.0499,  0.0691,  0.2331,  0.1059,
                         0.2002,  0.0814,  0.0600,  0.1339, -0.0126,  0.2261,  0.0820,  0.0792],
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                          [-0.2170, -0.2544, -0.0492],
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                         [[-0.0595, -0.0302, -0.0152],
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                         [[-0.0324, -0.1203, -0.1256],
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                          [ 0.1346,  0.1667,  0.0620]],
                
                         ...,
                
                         [[-0.0009, -0.0119, -0.0594],
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                          [ 0.0069,  0.1543,  0.0843]],
                
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                        [[[-0.0529, -0.0540, -0.0986],
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                         [[ 0.0468,  0.0536, -0.0088],
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                          [-0.0160,  0.0052, -0.0272]],
                
                         [[-0.0321, -0.0150, -0.0295],
                          [-0.0033, -0.0490, -0.0789],
                          [-0.0559, -0.0469, -0.0119]],
                
                         ...,
                
                         [[ 0.0198, -0.0530, -0.0669],
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                          [ 0.0254, -0.0084, -0.0581]],
                
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                        [[[-0.0164, -0.0702, -0.0414],
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                         [[ 0.0207, -0.0398,  0.0558],
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                         [[-0.0215,  0.0039,  0.1180],
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                          [-0.1951,  0.0656,  0.1305]],
                
                         ...,
                
                         [[-0.0539,  0.0277,  0.1282],
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                          [-0.2690, -0.1529, -0.0681]],
                
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                         [[-0.2183, -0.0499,  0.0782],
                          [-0.1409,  0.0008,  0.0312],
                          [-0.0418,  0.0101, -0.0207]]],
                
                
                        ...,
                
                
                        [[[ 0.0156,  0.1028,  0.1197],
                          [-0.0453, -0.0977,  0.0438],
                          [ 0.0078, -0.0952, -0.1755]],
                
                         [[ 0.0354,  0.0153, -0.0234],
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                         [[-0.0727, -0.1705, -0.2092],
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                          [ 0.0822,  0.2257,  0.1818]],
                
                         ...,
                
                         [[ 0.0473, -0.0188,  0.0460],
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                          [ 0.0694,  0.0054,  0.1502]],
                
                         [[ 0.1835,  0.1227,  0.0980],
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                          [ 0.0055, -0.2075, -0.2732]],
                
                         [[-0.1787, -0.1806, -0.0479],
                          [ 0.0867,  0.2297,  0.2666],
                          [-0.0556,  0.1080,  0.1119]]],
                
                
                        [[[ 0.0273, -0.0914, -0.0664],
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                          [-0.0478, -0.0736, -0.0950]],
                
                         [[-0.0204, -0.0465,  0.0530],
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                         [[ 0.0271,  0.0052,  0.0081],
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                          [-0.0229, -0.0465, -0.0353]],
                
                         ...,
                
                         [[-0.0282, -0.1465,  0.0287],
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                          [-0.0058, -0.0444, -0.0304]],
                
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                         [[-0.0686, -0.0996, -0.0948],
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                        [[[ 0.0902,  0.1045,  0.1187],
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                         [[-0.0202, -0.0058,  0.0956],
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                         [[-0.0768, -0.0530,  0.0310],
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                          [-0.0842, -0.0379, -0.0368]],
                
                         ...,
                
                         [[-0.1073, -0.0634, -0.1424],
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                          [ 0.0189,  0.0226,  0.0271]],
                
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                          [ 0.1461,  0.1518, -0.3584]],
                
                         [[-0.1749, -0.1744, -0.0146],
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                          [ 0.1629,  0.0601,  0.0379]]]], device='cuda:0')),
               ('conv_layers.7.bias',
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                         0.1087, -0.0540, -0.0853,  0.1101,  0.1470, -0.0783,  0.0681, -0.0280,
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                         ...,
                
                         [[ 0.0523,  0.1239,  0.0691],
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                        [[[-0.1047,  0.2020,  0.1547],
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                         [[-0.1366,  0.1355,  0.1824],
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                          [-0.0483, -0.0064, -0.1199]],
                
                         ...,
                
                         [[-0.0343,  0.0048, -0.0869],
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                         [[-0.0623,  0.0549,  0.1783],
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                        [[[-0.1458, -0.1944, -0.1346],
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                         [[ 0.0685,  0.0396, -0.0446],
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                         [[-0.0377, -0.1435,  0.1307],
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                          [ 0.0241,  0.0146,  0.0773]],
                
                         ...,
                
                         [[-0.1642, -0.0183, -0.0212],
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                          [ 0.2512,  0.3533,  0.1329]],
                
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                         [[-0.2098, -0.1373, -0.1250],
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                        ...,
                
                
                        [[[-0.0130, -0.1314,  0.0587],
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                         [[ 0.0266,  0.0258,  0.0261],
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                         [[ 0.0440,  0.0710,  0.1401],
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                          [-0.1003, -0.0841, -0.0385]],
                
                         ...,
                
                         [[ 0.0260,  0.0112,  0.0129],
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                          [-0.1520, -0.0342,  0.0677]],
                
                         [[ 0.0499,  0.0323,  0.0285],
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                          [-0.0863, -0.0549, -0.0300]],
                
                         [[ 0.0192, -0.0432,  0.0021],
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                          [-0.2211, -0.0120,  0.1216]]],
                
                
                        [[[-0.1075, -0.4029,  0.1284],
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                         [[-0.0227, -0.1098,  0.0024],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-0.2292, -0.1652, -0.0893],
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                        ...,
                
                
                        [[[ 0.0246, -0.0126,  0.0550],
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                         ...,
                
                         [[ 0.1029, -0.0076, -0.0068],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[ 1.2914e-02,  3.6835e-02,  4.5416e-02],
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                        ...,
                
                
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                         ...,
                
                         [[-5.7837e-02, -3.3123e-02,  4.5942e-02],
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                         ...,
                
                         [[-1.7610e-02,  3.5972e-02, -2.1824e-02],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-2.1661e-03,  2.4133e-01,  9.6310e-02],
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                         ...,
                
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                         ...,
                
                         [[-1.4599e-02, -2.9266e-01, -1.4289e-02],
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                         ...,
                
                         [[-8.1141e-02, -8.9312e-02, -1.5980e-01],
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                         ...,
                
                         [[-8.5918e-02, -4.3365e-02, -1.9855e-02],
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                        ...,
                
                
                        [[[-2.6563e-01, -2.0163e-01, -5.7454e-02],
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                         ...,
                
                         [[-6.2440e-03,  6.0522e-02,  3.5079e-02],
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                         ...,
                
                         [[-8.8589e-02,  3.3292e-02,  1.5766e-01],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[ 0.1855,  0.0507, -0.0565],
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                        ...,
                
                
                        [[[-0.1849,  0.0078, -0.0043],
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                         ...,
                
                         [[-0.1592, -0.0278, -0.2067],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[ 7.5039e-02,  1.0490e-01, -6.8445e-03],
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                         ...,
                
                         [[ 3.6465e-02, -2.7523e-02, -9.6472e-02],
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-0.1004, -0.0093, -0.0040],
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                        ...,
                
                
                        [[[-0.2068, -0.2282, -0.0381],
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                         ...,
                
                         [[-0.0498, -0.0333,  0.0678],
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                         ...,
                
                         [[-0.0626,  0.0601,  0.1518],
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-4.5884e-02,  2.8877e-02, -1.0192e-01],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         [[-1.0333e-01, -1.7641e-02, -6.9446e-02],
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                         [[ 9.3339e-02, -4.1237e-02,  3.7230e-02],
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                         ...,
                
                         [[-2.4374e-02, -8.1836e-02, -1.4704e-01],
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                         [[-2.7343e-01, -1.5820e-01, -1.9277e-01],
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                        [[[-4.4799e-03,  1.5062e-01, -1.0177e-01],
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                         [[-4.7051e-03,  1.1101e-01, -2.3412e-02],
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                         ...,
                
                         [[-2.2961e-01,  1.5339e-01,  2.2850e-01],
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                         ...,
                
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                         [[-8.7943e-02,  5.6119e-03,  2.0607e-02],
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                         [[-1.7558e-01, -1.2123e-01, -3.2048e-01],
                          [-1.1804e-01, -1.0303e-01, -4.8767e-02],
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                         ...,
                
                         [[ 0.0426, -0.0148,  0.0795],
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                        [[[ 0.0523,  0.0561,  0.0648],
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                         [[-0.1058,  0.0915,  0.1192],
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                         ...,
                
                         [[ 0.0212,  0.0168,  0.0055],
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                          [-0.0511, -0.1374, -0.0090]],
                
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                        [[[-0.1003, -0.1273, -0.0626],
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                         [[ 0.1539,  0.0952, -0.1005],
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                         ...,
                
                         [[ 0.2157,  0.0719,  0.0342],
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                        ...,
                
                
                        [[[-0.1459, -0.1228,  0.0448],
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                          [-0.0952,  0.1207,  0.1344]],
                
                         ...,
                
                         [[-0.0223, -0.0594, -0.0392],
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                          [-0.1875, -0.1800, -0.0338]],
                
                         [[-0.0340, -0.1138,  0.1194],
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                         [[-0.2922, -0.2055, -0.0405],
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                        [[[ 0.1127,  0.0504, -0.0362],
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                          [-0.1197,  0.1250, -0.0837]],
                
                         ...,
                
                         [[ 0.0318, -0.0049, -0.0376],
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                        [[[-0.0968, -0.0222,  0.0679],
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                         [[ 0.1118,  0.1362, -0.0464],
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                         [[-0.0153,  0.0331, -0.1626],
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                         ...,
                
                         [[ 0.0132,  0.0457,  0.0116],
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                         0.0680,  0.1283,  0.0914,  0.1671,  0.1941, -0.0083,  0.0314,  0.1580],
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                         ...,
                
                         [[-0.0814, -0.0112, -0.0343],
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                         [[-0.2395, -0.3122, -0.2750],
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                         [[-0.3153, -0.0304,  0.1760],
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                          [-0.2770, -0.0852,  0.0504]],
                
                         ...,
                
                         [[-0.1049,  0.0107, -0.0881],
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                        [[[-0.0752,  0.0225,  0.1765],
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                         ...,
                
                         [[-0.1601,  0.0704, -0.0401],
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                        ...,
                
                
                        [[[-0.1170, -0.0235,  0.0743],
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                         ...,
                
                         [[ 0.0104, -0.0510, -0.0166],
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                        [[[-0.0178, -0.1563, -0.0954],
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                         ...,
                
                         [[ 0.0286,  0.0804, -0.0111],
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                        [[[-0.0840, -0.0277, -0.1315],
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                          [-0.0041, -0.0730,  0.0015]],
                
                         ...,
                
                         [[-0.0217,  0.0005, -0.0672],
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                         ...,
                
                         [[ 0.0803,  0.0584,  0.1442],
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                         ...,
                
                         [[ 0.0756, -0.0496,  0.0357],
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-5.8057e-02, -1.8515e-01, -1.5655e-01],
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                        ...,
                
                
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                         ...,
                
                         [[-7.2444e-02,  7.2035e-02, -1.3005e-01],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
                        [[[ 0.1358, -0.0066, -0.0378],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[ 1.4828e-01, -8.1646e-02, -4.3921e-02],
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                        ...,
                
                
                        [[[-1.2487e-01, -4.9520e-02, -9.9742e-03],
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                         ...,
                
                         [[ 1.7282e-01,  1.0777e-01, -2.8937e-02],
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                         ...,
                
                         [[-9.1896e-02, -1.1359e-02,  5.3635e-02],
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                         ...,
                
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                         ...,
                
                         [[ 5.2827e-02,  7.4228e-02, -8.0520e-02],
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                         ...,
                
                         [[-1.6026e-01,  1.0256e-01,  2.6394e-01],
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                         ...,
                
                         [[-1.4452e-02, -1.4324e-01, -1.2445e-01],
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                        ...,
                
                
                        [[[-1.7846e-02, -1.1152e-01, -1.4668e-01],
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                         ...,
                
                         [[ 6.1684e-02, -2.7873e-02,  2.1109e-01],
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                         ...,
                
                         [[-6.8878e-02,  9.0436e-02,  1.3854e-01],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[ 0.0937, -0.0332, -0.0067],
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                        ...,
                
                
                        [[[ 0.0962, -0.0465,  0.1298],
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                         ...,
                
                         [[-0.1422, -0.1844, -0.2878],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
                        [[[-1.3759e-01,  2.7049e-03, -7.9909e-02],
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                         ...,
                
                         [[ 7.9566e-02,  1.3083e-01,  8.1429e-02],
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                         ...,
                
                         [[-1.5694e-01, -2.2109e-02, -9.1290e-02],
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-3.7617e-02, -3.5474e-03,  2.1929e-01],
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                         ...,
                
                         [[-2.0232e-01,  4.7026e-02, -5.2175e-02],
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                        ...,
                
                
                        [[[ 5.0872e-02,  4.6986e-02, -1.3631e-02],
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                         ...,
                
                         [[ 6.5318e-02, -1.1108e-01, -4.7450e-02],
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                         ...,
                
                         [[-1.5489e-01, -9.1927e-04, -3.9162e-02],
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[ 1.1487e-01,  2.5749e-02, -8.4589e-02],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         [[ 2.1981e-04, -9.6202e-02,  6.7938e-02],
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                        [[[-6.5784e-02, -6.7720e-02, -1.4863e-01],
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                         [[-9.3756e-02, -1.5404e-01, -5.5830e-02],
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                         [[-4.9989e-02, -1.3760e-01,  1.3018e-01],
                          [-1.3658e-01,  2.1095e-02,  1.4825e-01],
                          [-8.7601e-02,  6.2429e-02,  1.8856e-01]],
                
                         ...,
                
                         [[ 1.8084e-01, -1.3729e-02, -1.0221e-01],
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                         [[ 2.8055e-02, -7.1631e-02, -2.5416e-02],
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                         [[ 9.5062e-02, -1.8718e-01, -2.0110e-01],
                          [ 1.0355e-01, -1.8293e-01, -4.9956e-02],
                          [-1.9943e-02, -6.0394e-02,  2.8275e-02]]],
                
                
                        [[[ 7.6025e-02,  2.1604e-01,  1.3397e-01],
                          [-1.5362e-01,  1.1927e-03,  7.7186e-02],
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                         [[-9.5511e-02, -6.6674e-02,  5.2411e-02],
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                          [ 2.0657e-03,  1.8016e-01,  2.3774e-01]],
                
                         [[-2.1974e-01, -2.0275e-01, -3.6756e-03],
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                          [ 2.1843e-02,  1.0824e-01,  9.2206e-02]],
                
                         ...,
                
                         [[ 7.7463e-02,  2.9190e-02,  9.1800e-02],
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                          [ 9.6636e-02, -1.1431e-01, -2.3164e-01]],
                
                         [[-1.6865e-01, -1.5072e-01, -5.3525e-02],
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                         [[-1.4483e-01, -6.5377e-02,  8.9519e-02],
                          [-6.1641e-02, -7.3641e-02, -1.8194e-02],
                          [ 1.5254e-01,  1.1306e-01,  8.2455e-02]]],
                
                
                        ...,
                
                
                        [[[ 1.0196e-01,  2.9415e-01,  1.7580e-02],
                          [ 1.3073e-01, -1.3788e-02, -2.3153e-01],
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                         [[ 5.2919e-02,  1.2055e-01,  1.2647e-01],
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                         [[ 6.9171e-02,  6.0733e-02,  1.4136e-01],
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                          [ 6.3824e-03, -3.2937e-02, -9.4563e-02]],
                
                         ...,
                
                         [[-1.3070e-01, -3.8148e-02,  1.0482e-01],
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                         [[ 7.1075e-02,  7.5987e-02,  1.0126e-01],
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                         [[ 1.0149e-01,  7.4031e-02,  2.2899e-01],
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                        [[[-4.3373e-02,  1.1202e-01,  1.0004e-01],
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                         [[-4.0545e-02,  6.0561e-02,  7.0649e-02],
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                         [[-4.4209e-02,  2.4782e-02,  1.2781e-01],
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                          [-2.4934e-01,  1.4663e-01,  1.0130e-01]],
                
                         ...,
                
                         [[-1.2858e-02,  5.8047e-02,  6.3839e-02],
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                         [[ 1.1450e-01, -1.7879e-01,  1.2134e-01],
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                         [[ 3.3212e-02, -2.6778e-01, -1.0538e-01],
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                        [[[ 1.0410e-01, -1.5175e-01, -1.2419e-01],
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                         [[-3.7051e-01,  4.2474e-02,  1.8489e-02],
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                         [[-5.7867e-01, -7.6946e-02, -1.3647e-01],
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                         ...,
                
                         [[-1.1053e-01, -4.2484e-02, -7.7115e-02],
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                         [[-1.3023e-01, -9.4263e-02, -2.6341e-01],
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                         ...,
                
                         [[-0.0866,  0.0362,  0.0296],
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                        [[[-0.0635, -0.0659,  0.0174],
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                         ...,
                
                         [[ 0.0916,  0.1449,  0.1394],
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                        [[[-0.0285,  0.0937,  0.0414],
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                         [[-0.0810, -0.0368, -0.0127],
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                         [[-0.0244,  0.1248,  0.1266],
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                         ...,
                
                         [[-0.1133, -0.3351, -0.1310],
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                        ...,
                
                
                        [[[-0.2025, -0.1527, -0.1159],
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                         [[-0.1895,  0.0984,  0.0525],
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                         [[-0.2232, -0.1306, -0.0138],
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                          [-0.1549,  0.0323,  0.1190]],
                
                         ...,
                
                         [[ 0.0477,  0.0863, -0.0588],
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                         [[-0.1519,  0.0833, -0.1145],
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                         [[-0.0792, -0.0994, -0.3562],
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                        [[[-0.0906, -0.1880, -0.1391],
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                         [[-0.2600, -0.1466,  0.0780],
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                         ...,
                
                         [[ 0.0476, -0.1730, -0.0762],
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                        [[[-0.3031, -0.0268,  0.0912],
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                         ...,
                
                         [[-0.0193, -0.0056,  0.0750],
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                          [-0.1260, -0.3032, -0.3333]]]], device='cuda:0')),
               ('conv_layers.7.bias',
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                         ...,
                
                         [[-0.0865,  0.1743, -0.0090],
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                         ...,
                
                         [[-0.2428, -0.0463,  0.0063],
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                        [[[-0.0084, -0.1134, -0.0067],
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                         ...,
                
                         [[ 0.0147, -0.1150,  0.1347],
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                        ...,
                
                
                        [[[-0.0828,  0.1137, -0.0157],
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                         ...,
                
                         [[-0.1812,  0.0480,  0.0165],
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                        [[[-0.0859, -0.0618, -0.0627],
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                         ...,
                
                         [[ 0.0338, -0.0083, -0.0220],
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                         [[-0.0505, -0.0433,  0.0043],
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                        [[[ 0.0543,  0.0849,  0.1034],
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-0.1303,  0.1311,  0.0121],
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                        ...,
                
                
                        [[[-0.0274,  0.0942,  0.0401],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
                         [[-7.5031e-02, -1.6301e-01, -4.9285e-02],
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                         ...,
                
                         [[-5.1560e-02, -7.6638e-02, -4.4974e-01],
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-4.2764e-01, -1.8252e-01, -5.3818e-02],
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                         [[-1.3709e-01, -1.2899e-01, -4.2147e-01],
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                         [[-1.1859e-01, -4.0560e-02, -2.5686e-01],
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                        [[[-2.4083e-02, -5.6350e-02, -4.0480e-02],
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                         [[-8.2016e-02, -1.1950e-01, -1.4700e-02],
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                         ...,
                
                         [[ 2.9749e-03, -1.1248e-01, -1.1440e-01],
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                         [[-5.6076e-02, -4.9165e-02,  5.7913e-02],
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                        ...,
                
                
                        [[[-1.1542e-01,  1.6844e-02,  4.7652e-02],
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                         [[-2.2417e-01, -1.4917e-01, -6.3683e-02],
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                         [[-1.6537e-01, -8.7702e-02, -1.4567e-01],
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                         ...,
                
                         [[ 4.9002e-02, -6.6350e-03, -9.0734e-02],
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                         ...,
                
                         [[ 1.4216e-02,  1.6863e-01,  2.2824e-01],
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                         ...,
                
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                         [[-2.6017e-01, -4.4031e-02,  5.1768e-02],
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                         ...,
                
                         [[-0.0692, -0.1417, -0.2375],
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                         ...,
                
                         [[ 0.1964,  0.1940, -0.2568],
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                         ...,
                
                         [[-0.0827, -0.0973, -0.1233],
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                        ...,
                
                
                        [[[-0.1156, -0.0281, -0.0161],
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                         ...,
                
                         [[-0.1405, -0.2366, -0.1077],
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                        [[[-0.0947, -0.0417, -0.0512],
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                         ...,
                
                         [[-0.0085, -0.0934, -0.0703],
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                         ...,
                
                         [[-0.0667, -0.1000,  0.0809],
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                         ...,
                
                         [[-2.9058e-02,  1.9471e-02,  2.0946e-02],
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                         ...,
                
                         [[-1.9836e-02, -1.9742e-01, -1.3537e-01],
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                         ...,
                
                         [[-1.1036e-01, -2.4966e-02,  1.8081e-01],
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                        ...,
                
                
                        [[[-3.0123e-02,  9.3158e-02,  9.2010e-02],
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                         ...,
                
                         [[ 1.0777e-01,  1.3743e-01,  7.8031e-02],
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
                         [[ 1.4246e-01,  1.2386e-01, -6.2295e-02],
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                         ...,
                
                         [[-1.4037e-01, -1.5989e-01, -9.6803e-02],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-8.0514e-02,  2.7030e-02, -2.0252e-01],
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                        ...,
                
                
                        [[[-2.2832e-01,  1.7061e-03,  5.2144e-03],
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                         ...,
                
                         [[ 9.4202e-02,  1.1881e-01,  2.6569e-02],
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                         ...,
                
                         [[ 3.2467e-02, -4.1829e-02,  8.6170e-02],
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[ 1.8636e-02, -1.8370e-02, -5.1853e-02],
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                         ...,
                
                         [[ 4.6507e-02, -4.9108e-02, -1.6277e-02],
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                         [[-1.3509e-01, -1.1620e-01,  2.8598e-02],
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                        ...,
                
                
                        [[[-2.5472e-01, -1.8454e-01, -1.5949e-01],
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                         [[-1.0520e-01, -9.5025e-02, -2.4072e-01],
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                         [[ 1.7416e-01,  8.6497e-02, -4.4989e-02],
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                         ...,
                
                         [[-2.4744e-02,  1.1366e-01, -4.6140e-01],
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                        [[[-8.7764e-02, -2.4799e-02, -5.0480e-02],
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                         ...,
                
                         [[-7.6024e-02, -4.6939e-02, -5.7555e-02],
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                         ...,
                
                         [[-2.3363e-02, -2.2554e-02, -2.0785e-02],
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                         ...,
                
                         [[-0.0178, -0.0573, -0.0957],
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                         ...,
                
                         [[ 0.1031, -0.0699, -0.2907],
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                         ...,
                
                         [[ 0.1854,  0.0064, -0.1341],
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                        ...,
                
                
                        [[[ 0.1260, -0.0598, -0.1716],
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                         ...,
                
                         [[ 0.0253,  0.0303, -0.1192],
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                         ...,
                
                         [[-0.0196,  0.0303, -0.0824],
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-0.0086,  0.0652,  0.0307],
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                         ...,
                
                         [[-0.0900,  0.0151,  0.0589],
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                        ...,
                
                
                        [[[-0.0077,  0.0138, -0.0642],
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                         ...,
                
                         [[-0.0763,  0.0334,  0.0864],
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                         ...,
                
                         [[-0.1856, -0.1414, -0.0138],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
                         [[-1.1754e-02, -1.2776e-01, -1.8665e-01],
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                        ...,
                
                
                        [[[-1.5312e-01, -5.6534e-02, -7.8072e-02],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
                        [[[-3.2555e-02,  1.6457e-01, -2.0311e-02],
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                         ...,
                
                         [[ 2.7116e-01, -1.1151e-01, -6.0417e-02],
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                         ...,
                
                         [[ 1.6899e-01,  1.8257e-02, -6.3698e-04],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        ...,
                
                
                        [[[ 2.3137e-02, -7.6884e-02, -1.5399e-01],
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                         ...,
                
                         [[-3.9258e-02, -1.6766e-02,  7.0580e-03],
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                         ...,
                
                         [[ 2.8344e-02, -7.4201e-03, -3.7854e-01],
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                         ...,
                
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                         ...,
                
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                         ...,
                
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                        [[[-3.0715e-03, -3.7657e-02, -9.8320e-02],
                          [-7.8402e-02, -2.6468e-02, -9.2191e-03],
                          [-6.9625e-02, -5.7747e-02, -7.5805e-02]],
                
                         [[-2.2274e-02,  8.7470e-03, -7.6252e-02],
                          [-8.6493e-02,  2.6160e-02,  1.9582e-02],
                          [-5.8257e-02, -5.9879e-02, -1.3937e-02]],
                
                         [[ 2.8675e-02, -3.8858e-02,  1.1301e-02],
                          [-4.9586e-02,  3.4557e-02,  6.4728e-03],
                          [ 1.0786e-02,  2.2407e-02,  5.3677e-02]],
                
                         ...,
                
                         [[ 1.3728e-02, -5.2479e-02, -1.1010e-01],
                          [ 2.4051e-03, -3.3614e-02, -6.2793e-02],
                          [ 2.0625e-02,  1.1473e-03, -3.3369e-03]],
                
                         [[-4.0663e-03, -2.8863e-02, -4.0014e-02],
                          [-2.8524e-02,  1.4733e-02, -2.2825e-02],
                          [-1.1408e-02, -2.8542e-02, -8.2511e-02]],
                
                         [[-4.8357e-03, -6.0432e-02,  1.7796e-02],
                          [-6.9727e-04,  1.0095e-02, -1.9711e-02],
                          [ 1.2001e-02, -4.0031e-02, -1.5545e-02]]],
                
                
                        ...,
                
                
                        [[[-2.0078e-01,  2.1117e-02,  1.5347e-01],
                          [-1.5146e-01,  6.5237e-02,  1.8788e-01],
                          [ 2.3454e-02,  1.2866e-01,  2.9413e-01]],
                
                         [[-3.7031e-02, -1.1452e-01, -6.2603e-02],
                          [ 2.4561e-02,  4.0511e-03, -9.2671e-03],
                          [-7.9670e-03,  1.1119e-01,  1.6635e-01]],
                
                         [[ 1.4160e-01,  1.6737e-01,  1.3968e-01],
                          [ 2.1730e-01,  4.9700e-02, -8.7731e-02],
                          [-4.7046e-02, -9.5150e-02, -4.5136e-02]],
                
                         ...,
                
                         [[ 5.5272e-02,  4.6913e-02,  1.9110e-01],
                          [-1.3490e-02,  7.1670e-02,  9.7697e-02],
                          [ 9.4593e-02,  4.4740e-02,  9.3439e-02]],
                
                         [[ 1.0773e-01,  2.9414e-02,  1.3448e-01],
                          [-6.8102e-02, -1.6841e-01,  1.4036e-01],
                          [-6.8541e-02, -9.0082e-02,  1.3721e-01]],
                
                         [[ 7.9165e-03,  9.7715e-02,  1.9288e-01],
                          [-3.3795e-02,  6.0217e-02,  1.2659e-01],
                          [ 4.9798e-02,  2.0553e-02,  3.4552e-02]]],
                
                
                        [[[ 6.6777e-02,  1.6501e-01, -7.9637e-02],
                          [ 7.0596e-02,  3.7708e-02, -1.1672e-01],
                          [ 1.0682e-01,  8.5524e-03, -1.4350e-01]],
                
                         [[ 8.3364e-02,  6.5216e-02, -1.9231e-01],
                          [ 1.0413e-01, -5.5511e-02, -3.3764e-01],
                          [ 5.0924e-02, -1.0456e-01, -4.1149e-01]],
                
                         [[-3.6877e-01, -1.9218e-01, -1.5603e-01],
                          [-2.1344e-01, -9.0347e-02,  4.1147e-02],
                          [-2.4876e-01, -7.6765e-02,  1.4148e-01]],
                
                         ...,
                
                         [[ 8.9547e-02, -9.1532e-02, -1.9001e-01],
                          [ 3.7244e-02, -2.0003e-01, -1.0590e-01],
                          [ 2.2178e-01,  1.3772e-01, -6.1669e-02]],
                
                         [[-1.9552e-01, -7.7376e-02, -1.7308e-01],
                          [-4.6343e-02, -1.5503e-01, -1.2731e-01],
                          [-1.0114e-01, -2.4989e-01,  1.8386e-01]],
                
                         [[ 4.0021e-02, -1.2553e-01, -1.8682e-01],
                          [-9.2273e-03, -1.2105e-01, -1.3929e-01],
                          [ 1.3424e-01, -8.1632e-03,  1.2522e-02]]],
                
                
                        [[[ 4.9459e-02,  5.7277e-02, -1.0958e-01],
                          [ 1.8008e-02,  1.8347e-02,  5.9727e-03],
                          [ 1.3724e-02,  1.3408e-02,  8.1452e-02]],
                
                         [[-3.8002e-02, -1.4002e-01, -2.0876e-01],
                          [-7.2546e-02,  1.5576e-02,  1.0262e-02],
                          [ 2.2784e-05, -1.6702e-03,  1.5322e-01]],
                
                         [[-6.5118e-02,  1.8329e-01,  3.3285e-02],
                          [-1.2403e-02,  7.2599e-02, -2.6558e-01],
                          [-3.7059e-01, -2.6436e-02,  1.4574e-01]],
                
                         ...,
                
                         [[ 3.1124e-02,  1.1499e-01, -1.2980e-01],
                          [ 1.1193e-02,  4.3660e-02,  1.0134e-01],
                          [-5.5773e-02,  2.0391e-02,  1.8495e-01]],
                
                         [[-8.9290e-02, -2.5757e-01, -2.4744e-01],
                          [-5.0920e-02, -1.0168e-01, -1.1623e-02],
                          [-9.0944e-02, -2.0082e-01, -3.2128e-02]],
                
                         [[ 8.8311e-02,  7.7653e-02, -7.1598e-02],
                          [ 3.5708e-01,  1.4579e-01,  1.6296e-02],
                          [ 2.2355e-01,  1.4632e-01,  1.3167e-01]]]], device='cuda:0')),
               ('conv_layers.12.bias',
                tensor([-3.9502e-02,  2.3040e-02, -8.0021e-02, -1.3592e-02,  1.2175e-04,
                         2.5499e-02, -5.6343e-02, -1.9519e-02,  7.5878e-02, -8.7756e-02,
                         2.8813e-02,  1.3735e-01,  1.7172e-01, -3.1268e-03, -2.6470e-03,
                         7.8613e-02, -1.1964e-01, -1.2649e-01,  2.3784e-01, -2.7362e-03,
                         4.5999e-02, -1.1683e-01, -6.4516e-02, -1.0553e-01, -1.4797e-02,
                         1.1175e-01,  2.9950e-02, -5.2819e-02, -2.4161e-02, -7.4739e-02,
                        -8.0514e-02, -4.6515e-02], device='cuda:0')),
               ('fc_layers.0.weight',
                tensor([[ 0.0134, -0.0168,  0.1489,  ...,  0.0080, -0.1296, -0.1599],
                        [-0.0392, -0.0129,  0.0162,  ..., -0.0070,  0.0091,  0.0128],
                        [ 0.0989,  0.1119,  0.0658,  ..., -0.2474, -0.1497,  0.1268],
                        ...,
                        [ 0.0400,  0.0020,  0.0139,  ...,  0.0194, -0.0852, -0.0795],
                        [-0.1046, -0.0453, -0.0477,  ...,  0.0032,  0.0174,  0.1708],
                        [-0.0107, -0.0232, -0.0232,  ...,  0.0019,  0.0250,  0.0641]],
                       device='cuda:0')),
               ('fc_layers.0.bias',
                tensor([ 3.3737e-02, -6.3531e-02,  8.2412e-02, -4.8122e-02, -3.7942e-02,
                        -1.0233e-02, -1.3329e-01, -2.0446e-02,  1.6626e-01, -1.1495e-01,
                        -5.9927e-02, -6.0561e-02,  2.1325e-01, -3.8245e-02,  8.1060e-03,
                        -8.3573e-02,  1.3280e-02, -9.2221e-02, -4.2055e-02, -5.8392e-02,
                        -4.9213e-04,  2.1032e-01,  5.3515e-02,  4.4350e-03,  3.4158e-03,
                        -1.2594e-01,  1.0379e-01,  1.4527e-01, -6.8962e-02, -1.0548e-01,
                         2.0350e-02, -1.8688e-02, -7.5845e-03,  1.5687e-01, -1.9957e-02,
                         1.3485e-02, -7.1717e-02, -1.3712e-02, -1.1056e-01, -9.5263e-02,
                        -7.2991e-02, -7.0043e-02, -8.7112e-02,  5.4135e-04,  3.7090e-02,
                        -4.7928e-02, -9.0835e-03,  1.6565e-01, -1.6095e-03,  4.3089e-02,
                        -3.4354e-02,  1.9188e-01, -3.2153e-02,  3.8820e-02, -5.1198e-02,
                        -3.8640e-02, -1.0192e-02,  2.0344e-03,  5.8275e-02, -1.8172e-02,
                        -6.0892e-02,  1.4952e-02, -6.1340e-02, -8.1210e-03, -5.3202e-02,
                        -3.4612e-02,  6.7383e-02, -5.6015e-02,  1.2375e-02, -2.3155e-02,
                        -8.2325e-02, -5.7303e-02, -1.2783e-01,  1.3162e-02,  1.0232e-01,
                        -2.6725e-02, -8.8501e-02,  9.8558e-02, -2.3936e-02, -5.8733e-02,
                         9.0249e-02,  3.9059e-02, -6.8925e-02, -6.9687e-02, -2.1736e-02,
                        -9.0963e-03,  1.8455e-01,  6.0882e-02,  7.4398e-02, -4.9180e-02,
                        -1.9082e-03, -4.4387e-02,  1.0227e-01, -7.5234e-02, -2.9876e-02,
                        -5.0744e-02, -4.1421e-02, -2.2110e-02,  1.2090e-01, -5.4899e-02,
                         1.4753e-01, -6.1984e-02, -9.3792e-02, -9.1605e-03,  2.9590e-02,
                        -5.1517e-02, -5.2968e-02, -6.2034e-02, -4.5369e-02, -3.9578e-02,
                        -3.9806e-03,  2.8616e-03, -6.0046e-02, -5.5356e-03, -6.2036e-02,
                        -2.0452e-02,  2.3375e-02,  2.1804e-02, -6.2821e-02, -9.0789e-02,
                        -2.3709e-02, -3.8402e-02, -3.6805e-02, -7.1154e-03, -5.4095e-02,
                        -5.0098e-02, -1.3038e-02, -1.9683e-02, -3.4951e-02,  1.7124e-01,
                         7.8282e-03, -2.0733e-02, -6.0946e-02,  1.3737e-01,  2.9403e-01,
                        -5.5723e-02, -9.2122e-02, -2.7139e-02, -8.4328e-03,  1.6097e-01,
                        -2.5881e-02,  2.1426e-02,  1.0852e-02, -6.2416e-02,  5.9874e-03,
                         1.5238e-02, -5.2449e-02, -6.0418e-02, -4.9638e-02, -4.6189e-02,
                        -1.4336e-01, -5.0228e-02,  4.5250e-02, -2.3655e-02,  7.4587e-03,
                         1.2923e-01,  6.1328e-02,  5.7500e-02,  2.4194e-02, -3.2932e-02,
                        -1.0820e-02,  7.1242e-02,  5.6005e-02, -1.3878e-02,  2.8693e-01,
                        -5.2415e-02, -7.4341e-02, -1.4338e-02, -9.2830e-02, -7.6373e-03,
                        -5.4241e-03, -2.9904e-02, -1.9841e-02,  1.0855e-01, -8.1063e-02,
                        -3.7344e-02, -6.5324e-03,  1.3485e-01,  8.7682e-02, -2.4356e-02,
                         7.7916e-02,  1.2459e-01, -4.2520e-02,  5.5024e-02, -1.6802e-02,
                        -1.4720e-04,  2.0956e-02, -3.1374e-02, -1.0519e-01, -2.4640e-02,
                        -2.6931e-02,  1.4007e-01,  5.0989e-02, -4.5955e-02, -5.6816e-02,
                        -2.9890e-02,  7.2223e-02, -3.3948e-02,  1.8985e-02, -2.7125e-02,
                        -2.3935e-03, -2.8839e-02, -2.4901e-02,  4.0219e-02,  9.2962e-02,
                         9.5073e-02, -1.1031e-02, -6.2019e-02, -4.8016e-02, -1.0641e-01,
                         1.4060e-01, -1.1940e-02, -9.2820e-02, -1.1954e-01, -1.4386e-02,
                        -4.4997e-02, -1.7866e-02, -6.9411e-02, -5.5662e-02,  2.5875e-02,
                        -1.6177e-02, -2.9509e-02, -3.8361e-03, -1.0750e-02, -8.2689e-02,
                        -4.2440e-02, -6.7098e-02, -3.5123e-02,  4.8586e-03,  7.6470e-02,
                        -3.5329e-02, -7.1604e-03,  1.4326e-01, -5.2647e-02,  7.3201e-02,
                         1.5089e-02, -5.0674e-02,  1.0034e-01,  3.9439e-02, -2.4589e-02,
                         8.1575e-02,  7.8365e-04, -5.1379e-02, -5.6496e-02, -2.8328e-02,
                        -1.2267e-02, -5.8921e-03,  4.6208e-02, -3.5443e-02, -2.8610e-02,
                         1.1720e-01,  1.5015e-03,  4.7232e-02, -7.4842e-02,  1.7563e-01,
                        -6.1315e-02], device='cuda:0')),
               ('fc_layers.2.weight',
                tensor([[-0.1582, -0.0426,  0.1531,  ..., -0.0248, -0.0957, -0.0536],
                        [-0.0247, -0.0347,  0.2120,  ..., -0.0812, -0.1250, -0.0350],
                        [-0.0780, -0.0226, -0.1972,  ..., -0.0151,  0.2745,  0.0237],
                        ...,
                        [-0.0719, -0.0108, -0.0455,  ...,  0.0232, -0.1399,  0.0347],
                        [-0.0507, -0.0524, -0.0824,  ...,  0.1039, -0.0394,  0.0593],
                        [ 0.3541, -0.0756, -0.1536,  ...,  0.1519, -0.1378, -0.0454]],
                       device='cuda:0')),
               ('fc_layers.2.bias',
                tensor([-0.1314,  0.0604,  0.2413,  0.1777,  0.0794, -0.1184, -0.0803, -0.1174,
                         0.0028, -0.0729], device='cuda:0'))])}]

candidate_epoch_num = [20, 40, 60, 80]
candidate_lr = [1e-3, 1e-4, 1e-5, 1e-6]

combinations = list(itertools.product(candidate_epoch_num, candidate_lr))
for combo in combinations:
    print(f"[{combo[0]}, {combo[1]:.0e}]", end=" ")

[20, 1e-03] [20, 1e-04] [20, 1e-05] [20, 1e-06] [40, 1e-03] [40, 1e-04] [40, 1e-05] [40, 1e-06] [60, 1e-03] [60, 1e-04] [60, 1e-05] [60, 1e-06] [80, 1e-03] [80, 1e-04] [80, 1e-05] [80, 1e-06]

def run_experiment(candidate_epoch_num, candidate_lr):
    combinations = list(itertools.product(candidate_epoch_num, candidate_lr))
    experiments = [] # Experiment Instances
    
    for combo in combinations:
        print(f"Performing Experiment: epoch_num={combo[0]}, lr={combo[1]}")
        exp_model = SmallVGG().to(device)
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.Adam(exp_model.parameters(), lr=combo[1])
        num_epochs = combo[0]
    
        train_losses, test_losses = train_and_evaluate(exp_model, train_loader, test_loader, criterion, optimizer, num_epochs)

        # One experiment instance
        experiments.append({
            "num_epoch": combo[0],
            "lr": combo[1],
            "train_losses": train_losses,
            "test_losses": test_losses,
            "model_state_dict": exp_model.state_dict()
        })
        
        # Prevent CUDA Mem Leak
        del exp_model, criterion, optimizer
        torch.cuda.empty_cache()
        
    return experiments

experiments = run_experiment(candidate_epoch_num, candidate_lr)
time_str = str(time.time()).replace(".","")
torch.save(experiments, f"./models/experiments_{time_str}.pth")

Performing Experiment: epoch_num=20, lr=0.001

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:16<00:00, 34.61it/s]

Epoch[1/20], Train Loss:273.6250, Test Loss:226.2518

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:16<00:00, 34.61it/s]

Epoch[2/20], Train Loss:192.3438, Test Loss:165.3802

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:17<00:00, 32.26it/s]

Epoch[3/20], Train Loss:158.9808, Test Loss:146.8151

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:16<00:00, 34.58it/s]

Epoch[4/20], Train Loss:144.1323, Test Loss:133.9099

100%|████████████████████████████████████████████████████████████████████████████████| 573/573 [00:16<00:00, 35.24it/s]

loaded_experiments = torch.load("./models/experiments_17296227919579012.pth")

D:\Temps\temp\ipykernel_68752\2506138615.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  loaded_experiments = torch.load("./models/experiments_17296227919579012.pth")

plot_el(loaded_experiments)

def get_experiment_results(loaded_experiments, extra_loader):
    experiment_results = []
    for i, exp in enumerate(loaded_experiments):
        pred_scores, true_labels_cpu, pred_labels_cpu = get_predictions(exp["model_state_dict"], extra_loader)
        print(f"Experiment {i+1}, num_epoch={exp['num_epoch']}, lr={exp['lr']}")
        print("First 100 true labels:")
        [print(num, end=" ") for num in true_labels_cpu[:100]]
        print("...\n")
    
        print("First 100 true predictions:")
        [print(num, end=" ") for num in pred_labels_cpu[:100]]
        print("...\n")
    
        print("First 5 prediction Probabilities:")
        [print(num, end=" ") for num in pred_scores[:5]]
        print("...")
    
        experiment_results.append({
            "epoch_num": exp['num_epoch'],
            "lr": exp['lr'],
            "true_labels": true_labels_cpu,
            "pred_labels": pred_labels_cpu,
            "pred_scores": pred_scores
        })
    
        del pred_scores, true_labels_cpu, pred_labels_cpu
        torch.cuda.empty_cache()
    return experiment_results

experiment_results = get_experiment_results(loaded_experiments, extra_loader)

100%|████████████████████████████████████████████████████████████████████████████████| 469/469 [00:08<00:00, 57.57it/s]

Experiment 1, num_epoch=20, lr=0.001
First 100 true labels:
4 7 8 7 1 1 7 4 3 0 2 8 8 3 1 1 7 0 8 1 5 6 4 4 4 6 3 4 4 3 0 1 7 6 0 1 1 0 5 7 5 1 8 5 5 2 9 6 1 5 2 3 5 3 6 9 2 3 4 1 7 7 3 1 2 2 0 1 1 3 1 5 1 1 9 9 4 8 0 5 1 3 8 2 9 5 6 0 7 8 3 0 6 4 0 3 1 1 0 0 ...

First 100 true predictions:
2 1 8 7 1 7 7 7 3 0 2 8 8 3 1 1 7 1 3 7 5 6 4 4 4 6 6 8 4 3 2 1 7 6 5 1 1 0 5 7 5 1 8 5 5 2 9 6 1 5 2 3 5 2 6 5 2 3 4 7 7 7 3 1 2 2 0 1 1 3 1 5 2 1 8 2 2 8 0 5 1 3 8 2 0 5 6 0 7 8 3 0 9 4 0 3 7 1 6 0 ...

First 5 prediction Probabilities:
[0.017714930698275566, 0.11971186846494675, 0.24600271880626678, 0.1634218990802765, 0.10604322701692581, 0.09993854910135269, 0.07444882392883301, 0.09129485487937927, 0.03048068843781948, 0.05094243958592415] [0.03476187214255333, 0.4355396628379822, 0.013863475993275642, 0.009521962143480778, 0.05131062492728233, 0.003921713214367628, 0.01304430142045021, 0.4110223054885864, 0.010886728763580322, 0.01612740010023117] [2.926520484280992e-11, 1.9651599791892238e-10, 1.1518684672751078e-08, 1.6825642035200872e-07, 1.5297356437127263e-10, 3.0309657561566894e-10, 7.056090112200764e-08, 1.1634063157295316e-10, 0.9999996423721313, 3.558699646077912e-08] [2.3501052215380014e-09, 1.2330511935942923e-06, 3.8137210367494845e-07, 1.2438673593351268e-06, 1.8493298057364882e-06, 2.4140272003592145e-09, 1.900778645591572e-09, 0.9999953508377075, 6.7932157676864335e-09, 4.1174885723194166e-09] [0.06164313107728958, 0.7637706995010376, 0.0017260065069422126, 0.012882382608950138, 0.04687739908695221, 0.003659889567643404, 0.006159364711493254, 0.09059558063745499, 0.009119569323956966, 0.0035659889690577984] ...

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Experiment 2, num_epoch=20, lr=0.0001
First 100 true labels:
4 7 8 7 1 1 7 4 3 0 2 8 8 3 1 1 7 0 8 1 5 6 4 4 4 6 3 4 4 3 0 1 7 6 0 1 1 0 5 7 5 1 8 5 5 2 9 6 1 5 2 3 5 3 6 9 2 3 4 1 7 7 3 1 2 2 0 1 1 3 1 5 1 1 9 9 4 8 0 5 1 3 8 2 9 5 6 0 7 8 3 0 6 4 0 3 1 1 0 0 ...

First 100 true predictions:
1 2 5 7 1 1 1 1 1 0 2 8 1 3 1 3 7 1 5 7 7 3 4 4 4 4 3 1 4 6 0 1 1 6 0 1 1 0 5 1 5 1 8 5 5 2 9 6 3 5 9 3 5 3 3 9 4 3 4 1 1 7 3 7 2 2 5 1 1 3 1 6 1 1 9 1 2 2 1 3 1 3 8 2 0 6 1 0 7 8 2 1 6 4 0 3 1 1 0 0 ...

First 5 prediction Probabilities:
[0.04172287881374359, 0.3212367296218872, 0.11267822980880737, 0.08403488993644714, 0.1291959136724472, 0.05936244875192642, 0.05597791448235512, 0.11382696032524109, 0.04099001735448837, 0.04097403585910797] [0.008109464310109615, 0.15274515748023987, 0.29611915349960327, 0.12467671930789948, 0.020193910226225853, 0.09674064815044403, 0.08162152767181396, 0.03990790247917175, 0.07686871290206909, 0.10301680862903595] [0.001103568123653531, 0.0018934233812615275, 0.027225932106375694, 0.10065190494060516, 0.00462863314896822, 0.680852472782135, 0.1441899836063385, 0.002218537963926792, 0.019375605508685112, 0.01785995252430439] [7.776140478199522e-07, 0.00023751752451062202, 1.265541754946753e-06, 7.943081072880886e-07, 4.357044872449478e-06, 2.038133661841357e-09, 1.3706218382480984e-08, 0.9997550845146179, 3.96911339350936e-08, 7.134497792549155e-08] [0.004980664700269699, 0.9530691504478455, 0.0012971817050129175, 0.0018052858067676425, 0.032232996076345444, 0.00013973248132970184, 0.0015251366421580315, 0.003493305528536439, 9.582896018400788e-05, 0.0013607727596536279] ...

100%|████████████████████████████████████████████████████████████████████████████████| 469/469 [00:07<00:00, 62.15it/s]

Experiment 3, num_epoch=20, lr=1e-05
First 100 true labels:
4 7 8 7 1 1 7 4 3 0 2 8 8 3 1 1 7 0 8 1 5 6 4 4 4 6 3 4 4 3 0 1 7 6 0 1 1 0 5 7 5 1 8 5 5 2 9 6 1 5 2 3 5 3 6 9 2 3 4 1 7 7 3 1 2 2 0 1 1 3 1 5 1 1 9 9 4 8 0 5 1 3 8 2 9 5 6 0 7 8 3 0 6 4 0 3 1 1 0 0 ...

First 100 true predictions:
4 1 2 1 1 1 1 1 5 1 2 2 5 1 1 1 7 1 1 1 5 1 1 1 1 1 1 2 1 3 1 1 1 1 7 1 1 1 2 7 6 1 2 5 5 2 3 7 4 3 2 2 7 1 2 3 2 1 4 1 1 7 6 1 5 2 1 1 1 2 1 7 1 1 3 4 3 1 1 5 1 6 4 2 1 1 1 1 7 1 2 1 2 1 4 2 3 1 1 5 ...

First 5 prediction Probabilities:
[0.03734436258673668, 0.04948404058814049, 0.21611124277114868, 0.10869403183460236, 0.2592947781085968, 0.06012629717588425, 0.07799053937196732, 0.011390982195734978, 0.08965682238340378, 0.08990683406591415] [0.11409001797437668, 0.4766749143600464, 0.0807449221611023, 0.050620533525943756, 0.07312004268169403, 0.01621292717754841, 0.0411507673561573, 0.06620706617832184, 0.047097332775592804, 0.03408142924308777] [0.018800511956214905, 0.010320345871150494, 0.29691198468208313, 0.18822698295116425, 0.054632868617773056, 0.14778466522693634, 0.09250607341527939, 0.01992134563624859, 0.08956916630268097, 0.08132601529359818] [0.06254780292510986, 0.5713654160499573, 0.06322319805622101, 0.04719596356153488, 0.015861205756664276, 0.013016232289373875, 0.04328763857483864, 0.13692724704742432, 0.034063659608364105, 0.012511675246059895] [0.09457781165838242, 0.42980247735977173, 0.10134153068065643, 0.07567155361175537, 0.025417957454919815, 0.017216505482792854, 0.03426406905055046, 0.15485386550426483, 0.04253013804554939, 0.02432398498058319] ...

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Experiment 4, num_epoch=20, lr=1e-06
First 100 true labels:
4 7 8 7 1 1 7 4 3 0 2 8 8 3 1 1 7 0 8 1 5 6 4 4 4 6 3 4 4 3 0 1 7 6 0 1 1 0 5 7 5 1 8 5 5 2 9 6 1 5 2 3 5 3 6 9 2 3 4 1 7 7 3 1 2 2 0 1 1 3 1 5 1 1 9 9 4 8 0 5 1 3 8 2 9 5 6 0 7 8 3 0 6 4 0 3 1 1 0 0 ...

First 100 true predictions:
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...

First 5 prediction Probabilities:
[0.06283650547266006, 0.2016061246395111, 0.14883245527744293, 0.11737629026174545, 0.1013929545879364, 0.09233619272708893, 0.07597127556800842, 0.07484865933656693, 0.06537968665361404, 0.05941982939839363] [0.06316563487052917, 0.20025570690631866, 0.14844679832458496, 0.11725891381502151, 0.10155297815799713, 0.09246815741062164, 0.07616836577653885, 0.07512465119361877, 0.06575461477041245, 0.059804175049066544] [0.06319037824869156, 0.19966019690036774, 0.14822980761528015, 0.11717073619365692, 0.10161317139863968, 0.09264856576919556, 0.07626385986804962, 0.07521895319223404, 0.06593448668718338, 0.06006978079676628] [0.05600240081548691, 0.22651784121990204, 0.1589825302362442, 0.11821643263101578, 0.09990055859088898, 0.08916525542736053, 0.07033000141382217, 0.06938424706459045, 0.05854524299502373, 0.052955422550439835] [0.0570356510579586, 0.22363786399364471, 0.15825268626213074, 0.11802932620048523, 0.10065381973981857, 0.08910296112298965, 0.07074697315692902, 0.07010345160961151, 0.05910445749759674, 0.053332824259996414] ...

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Experiment 5, num_epoch=40, lr=0.001
First 100 true labels:
4 7 8 7 1 1 7 4 3 0 2 8 8 3 1 1 7 0 8 1 5 6 4 4 4 6 3 4 4 3 0 1 7 6 0 1 1 0 5 7 5 1 8 5 5 2 9 6 1 5 2 3 5 3 6 9 2 3 4 1 7 7 3 1 2 2 0 1 1 3 1 5 1 1 9 9 4 8 0 5 1 3 8 2 9 5 6 0 7 8 3 0 6 4 0 3 1 1 0 0 ...

First 100 true predictions:
4 1 8 7 1 7 7 4 3 2 2 8 1 3 6 1 7 0 8 1 5 6 4 4 4 6 3 4 4 9 0 1 7 6 0 7 1 0 5 3 6 1 4 5 5 2 9 6 1 5 2 2 5 3 6 9 2 3 4 1 7 1 3 1 2 2 0 1 1 1 1 5 2 1 9 2 9 8 0 5 7 3 2 9 1 5 6 0 7 8 3 0 2 4 0 3 1 1 0 0 ...

First 5 prediction Probabilities:
[0.04449288547039032, 0.07780998200178146, 0.06755146384239197, 0.1529538780450821, 0.3235415518283844, 0.07038334757089615, 0.09183072298765182, 0.040107257664203644, 0.08839812874794006, 0.0429307222366333] [0.016988875344395638, 0.48411113023757935, 0.033633965998888016, 0.19107411801815033, 0.10796966403722763, 0.003598700975999236, 0.0012069017393514514, 0.1463702917098999, 0.002807996701449156, 0.01223838236182928] [0.0015220834175124764, 3.07643786072731e-05, 0.0006338836392387748, 0.001289050211198628, 1.1900595353608878e-07, 0.0003679135988932103, 0.053039468824863434, 3.0722421229256724e-07, 0.9423934817314148, 0.000722900265827775] [0.005543236155062914, 0.0379282645881176, 0.014228818006813526, 0.00858987681567669, 0.07698293030261993, 0.005204638000577688, 0.018007274717092514, 0.7500907182693481, 0.07028057426214218, 0.013143601827323437] [0.0029470503795892, 0.7723882794380188, 0.0017544578295201063, 0.007224154658615589, 0.0503050871193409, 0.00037459106533788145, 0.0022835498675704002, 0.15914276242256165, 0.0024723540991544724, 0.0011077923700213432] ...

plot_cm(experiment_results, ['epoch_num', 'lr'])

plot_pr(experiment_results, ['epoch_num', 'lr'])

plot_roc_auc(experiment_results, ['epoch_num','lr'], "macro_micro")

plot_roc_auc(experiment_results, ['epoch_num','lr'], "all")

CISC3024 Pattern Recognition Final Project¶

Group Members:¶

0. Project Setup¶

0.1 Packages & Device¶

0.2 Global Configurations¶

1. Data Processing and Augmentation¶

1.1 Download Datasets¶

1.2 Peak A Data¶

2. Neural Network¶

2.1 Model Structure¶

2.2 Train and Evaluate Function¶

2.3 Get Predictions¶

2.4 Get Metrics¶

3. Experiments¶

3.0 Preparation¶

3.0.1 Plot Functions¶

Epoch-Loss Curves¶

Get Experiment Results¶

Confusion Matrix¶

Precision-Recall Curve¶

ROC-AUC Curve¶

3.0.1 Datasets¶

3.1 Experiment 1: Optimizer¶

3.2 Experiment 2: Epoch Number and Learning Rate¶

3.2.1 Experiment 2-1: Rough Search¶

3.2.1-1 Epoch-Loss Curve¶

3.2.1-2 Confusion Matrix¶

3.2.1-3 Precision-Recall Curve¶

3.2.2 Experiment 2-2: Detailed¶

3.2.2-1 Epoch-Loss Curve¶

3.2.2-2 Confusion Matrix¶

3.2.2-3 Precision-Recall Curve¶

3.2.2-4 ROC-AUC Curve¶

3.3 Experiment 3: Transform¶

3.3.1 Experiment 3-1: Angles & Crops¶

3.3.2 Experiment 3-2: Ratios & Bias¶

(Deprecated) 3.2 Experiment 2: Epoch Number and Learning Rate¶

3.2.1 Design Hyper Parameger¶

3.2.2 Train Models¶

3.2.3 Load Experiments¶

3.2.4 Apply Model, Get Result¶

3.2.5 Confusion Matrix¶

3.2.6 Precision-Recall Curve¶

3.2.7 ROC AUC Curve¶